import pandas as pd
import numpy as np
import json
import datetime

from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
from sklearn.metrics import silhouette_score, mean_squared_error, mean_absolute_error, r2_score
from sklearn.metrics.pairwise import haversine_distances
from sklearn.neighbors import BallTree

import plotly.express as px
import plotly.graph_objects as go

import geopandas as gpd
from shapely.geometry import Point, Polygon, MultiPoint, box
from folium import GeoJson, Marker
from geopy.distance import geodesic
import pygeohash as pgh
import geohash2 as geohash

from math import radians, cos, sin, asin, sqrt
from scipy.spatial import cKDTree
from scipy.stats import chi2_contingency

from prophet import Prophet

from PIL import Image
from IPython.display import display

from tqdm import tqdm
import pyarrow
import openpyxl

df_station = pd.read_csv(r"C:\Users\asus\Desktop\5\2\DM\Homework\data\data\stations.csv")

df = pd.read_parquet(r"C:\Users\asus\Desktop\5\2\DM\Homework\data\data\daily-rent.parquet")


metro_df= pd.read_csv(r"C:\Users\asus\Desktop\5\2\DM\Homework\data\data\Metro_Bus_Stops.csv")
shuttle_df = pd.read_csv(r"C:\Users\asus\Desktop\5\2\DM\Homework\data\data\Shuttle_Bus_Stops.csv")

weather_df = pd.read_csv(r"C:\Users\asus\Desktop\5\2\DM\Homework\data2\Washington,DC,USA 2024-01-01 to 2024-12-31.csv") 

locations_df = pd.read_csv(r"C:\Users\asus\Desktop\5\2\DM\Homework\data\data\Capital_Bikeshare_Locations.csv")

cbd_gdf = gpd.read_file(r"C:\Users\asus\Desktop\5\2\DM\Homework\data2\Washington_DC_Boundary_Stone_Area.geojson")
parking_zones_gdf = gpd.read_file(r"C:\Users\asus\Desktop\5\2\DM\Homework\data\data\Residential_and_Visitor_Parking_Zones.geojson")
dc_boundary = gpd.read_file(r"C:\Users\asus\Desktop\5\2\DM\Homework\data2\Washington_DC_Boundary_Stone_Area.geojson")
Business_boundary =gpd.read_file(r"C:\Users\asus\Desktop\5\2\DM\Homework\data2\DDOT_Central_Business_District.geojson")

len(df)

6114323

df.dtypes

ride_id               string[python]
rideable_type         string[python]
started_at            datetime64[ns]
ended_at              datetime64[ns]
start_station_name    string[python]
start_station_id      string[python]
end_station_name      string[python]
end_station_id        string[python]
start_lat                    float64
start_lng                    float64
end_lat                      float64
end_lng                      float64
member_casual         string[python]
dtype: object

df['end_station_id'] = pd.to_numeric(df['end_station_id'], errors='coerce')


df['start_station_id'] = pd.to_numeric(df['start_station_id'], errors='coerce')

def clean_name(name):
    return str(name).strip().lower().replace('\xa0', '').replace('\n', '').replace('\r', '')

df['start_station_name'] = df['start_station_name'].map(clean_name)

df['end_station_name'] = df['end_station_name'].map(clean_name)

df_station['name'] = df_station['name'].map(clean_name)

df.isna().sum()

ride_id                     0
rideable_type               0
started_at                  0
ended_at                    0
start_station_name          0
start_station_id      1190966
end_station_name            0
end_station_id        1237694
start_lat                   0
start_lng                   0
end_lat                  4650
end_lng                  4650
member_casual               0
dtype: int64

df['start_station_name'] = df['start_station_name'].replace('<na>', np.nan)
df['end_station_name'] = df['end_station_name'].replace('<na>', np.nan)

df.isna().sum()

ride_id                     0
rideable_type               0
started_at                  0
ended_at                    0
start_station_name    1190966
start_station_id      1190966
end_station_name      1236167
end_station_id        1237694
start_lat                   0
start_lng                   0
end_lat                  4650
end_lng                  4650
member_casual               0
dtype: int64

invalid_mask = ~(
    df['start_lat'].between(38, 40) & 
    df['start_lng'].between(-78, -76)
)


print(f"عدد السطور التي لا تحقق الشرط الجغرافي: {invalid_mask.sum()}")


invalid_rows = df[invalid_mask]
print(invalid_rows[['start_station_name', 'start_lat', 'start_lng']].head(10))


df.loc[invalid_mask, ['start_lat', 'start_lng']] = np.nan

عدد السطور التي لا تحقق الشرط الجغرافي: 60
                                 start_station_name  start_lat  start_lng
74999                          glebe rd & 11th st n   0.000000   0.000000
142242               georgia & new hampshire ave nw   0.000000   0.000000
215570                         14th & belmont st nw   0.000000   0.000000
658823   pentagon city metro / 12th st & s hayes st   0.000000   0.000000
697252                       m st & delaware ave ne   0.000000   0.000000
728005                20th & o st nw / dupont south   0.000000   0.000000
781344                               17th & k st nw   0.000000   0.000000
814686                  new hampshire ave & t st nw   0.000000   0.000000
913346                columbia pike & s highland st   0.000000   0.000000
1003849             mount vernon ave & e nelson ave -81.346429 -78.366928

invalid_mask = ~(
    df['end_lat'].between(38, 40) & 
    df['end_lng'].between(-78, -76)
)


print(f"عدد السطور التي لا تحقق الشرط الجغرافي: {invalid_mask.sum()}")


invalid_rows = df[invalid_mask]
print(invalid_rows[['end_station_name', 'end_lat', 'end_lng']].head(10))


df.loc[invalid_mask, ['end_lat', 'end_lng']] = np.nan

عدد السطور التي لا تحقق الشرط الجغرافي: 4727
     end_station_name  end_lat  end_lng
16                NaN      NaN      NaN
36                NaN      NaN      NaN
275               NaN      NaN      NaN
297               NaN      NaN      NaN
309               NaN      NaN      NaN
1972              NaN      NaN      NaN
1995              NaN      NaN      NaN
1996              NaN      NaN      NaN
2018              NaN      NaN      NaN
2022              NaN      NaN      NaN

def calculate_station_centroids(df):

    start_df = df[['start_station_name', 'start_lat', 'start_lng']].rename(
        columns={'start_station_name': 'station_name', 'start_lat': 'lat', 'start_lng': 'lng'}
    )
    end_df = df[['end_station_name', 'end_lat', 'end_lng']].rename(
        columns={'end_station_name': 'station_name', 'end_lat': 'lat', 'end_lng': 'lng'}
    )
    

    combined = pd.concat([start_df, end_df], ignore_index=True)
    combined = combined.dropna(subset=['station_name', 'lat', 'lng']).drop_duplicates()

    def centroid(group):
        points = [Point(xy) for xy in zip(group['lng'], group['lat'])]
        if len(points) == 1:
            return points[0]
        else:
            return MultiPoint(points).centroid
    
    centroids = (
        combined.groupby('station_name', group_keys=False)
        .apply(centroid)
        .reset_index(name='geometry')
    )
    
    gdf_centroids = gpd.GeoDataFrame(centroids, geometry='geometry', crs='EPSG:4326')
    gdf_centroids['centroid_lat'] = gdf_centroids.geometry.y
    gdf_centroids['centroid_lng'] = gdf_centroids.geometry.x
    
    return gdf_centroids[['station_name', 'centroid_lat', 'centroid_lng']]


station_centroids = calculate_station_centroids(df)


df = df.merge(
    station_centroids.rename(columns={
        'station_name': 'start_station_name',
        'centroid_lat': 'start_lat',
        'centroid_lng': 'start_lng'
    }),
    on='start_station_name',
    how='left'
)

df = df.merge(
    station_centroids.rename(columns={
        'station_name': 'end_station_name',
        'centroid_lat': 'end_lat',
        'centroid_lng': 'end_lng'
    }),
    on='end_station_name',
    how='left'
)

unique_start_coords_count = df[['start_station_name','start_lat_y', 'start_lng_y']].drop_duplicates().dropna().shape[0]
print(f"عدد الأزواج الفريدة من خطوط الطول والعرض في بيانات البداية: {unique_start_coords_count}")

unique_end_coords_count = df[['end_station_name','end_lat_y', 'end_lng_y']].drop_duplicates().dropna().shape[0]
print(f"عدد الأزواج الفريدة من خطوط الطول والعرض في بيانات النهاية: {unique_end_coords_count}")

عدد الأزواج الفريدة من خطوط الطول والعرض في بيانات البداية: 817
عدد الأزواج الفريدة من خطوط الطول والعرض في بيانات النهاية: 817

print(df['started_at'].dt.year.value_counts())
print(df['ended_at'].dt.year.value_counts())

started_at
2024    6114303
1970         20
Name: count, dtype: int64
ended_at
2024    6114299
2030         20
2000          4
Name: count, dtype: int64

df = df[
    ~(
        (df['started_at'].dt.year == 1970) |
        (df['ended_at'].dt.year == 2030)|
       ( df['ended_at'].dt.year == 2000)

    )
]

print(df['started_at'].dt.year.value_counts())
print(df['ended_at'].dt.year.value_counts())

started_at
2024    6114279
Name: count, dtype: int64
ended_at
2024    6114279
Name: count, dtype: int64

df_station_sorted = df_station.sort_values(by=['name', 'id'])

df_station_unique = df_station_sorted.drop_duplicates(subset=['name'], keep='first').reset_index(drop=True)

df_station = df_station.sort_values(by=['name', 'id']).drop_duplicates(subset=['name'], keep='first').reset_index(drop=True)

num_unique_pairs = df_station[['id' ]].drop_duplicates().shape[0]
print(f"عدد القيم الفريدة   (id): {num_unique_pairs}")

عدد القيم الفريدة   (id): 803

num_unique_pairs = df_station[['id', 'name' ]].drop_duplicates().shape[0]
print(f"عدد القيم الفريدة لكل زوج (id, name): {num_unique_pairs}")

عدد القيم الفريدة لكل زوج (id, name): 819

end_names = set(df['end_station_name'].dropna().unique())
station_names = set(df_station['name'].dropna().unique())
missing_in_inside_dc =station_names - end_names
print(f"عددها: {len(missing_in_inside_dc)}")
for name in sorted(missing_in_inside_dc):
    print(" in df_station:", name)

عددها: 2
 in df_station: bowdoin ave & calvert rd/ college park metro
 in df_station: tysons west transit center

start_names = set(df['start_station_name'].dropna().unique())
station_names = set(df_station['name'].dropna().unique())
missing_in_inside_dc =station_names - start_names
print(f"عددها: {len(missing_in_inside_dc)}")
for name in sorted(missing_in_inside_dc):
    print("in df_station:", name)

عددها: 2
in df_station: eastern market metro/pillar
in df_station: tech trailer v-1

df_end = df[['end_station_id', 'end_station_name']].drop_duplicates()
merged = df_end.merge(df_station[['id', 'name']], left_on='end_station_name', right_on='name', how='inner')
mismatched_ids = merged[merged['end_station_id'] != merged['id']]
print(mismatched_ids[['end_station_name', 'end_station_id', 'id']])

                         end_station_name  end_station_id     id
871      randle circle & minnesota ave se           31799  31702
1107  john mccormack rd & michigan ave ne           31528  31502

df_end = df[['start_station_id', 'start_station_name']].drop_duplicates()
merged = df_end.merge(df_station[['id', 'name']], left_on='start_station_name', right_on='name', how='inner')
mismatched_ids = merged[merged['start_station_id'] != merged['id']]
print(mismatched_ids[['start_station_name', 'start_station_id', 'id']])

                      start_station_name  start_station_id     id
595     randle circle & minnesota ave se             31799  31702
801  john mccormack rd & michigan ave ne             31528  31502

df.loc[df['start_station_name'].str.lower() == 'john mccormack rd & michigan ave ne', 'start_station_id'] = 31502
df.loc[df['start_station_name'].str.lower() == 'randle circle & minnesota ave se', 'start_station_id'] = 31702
df.loc[df['end_station_name'].str.lower() == 'john mccormack rd & michigan ave ne', 'end_station_id'] = 31502
df.loc[df['end_station_name'].str.lower() == 'randle circle & minnesota ave se', 'end_station_id'] =31702

station_lookup = df[['start_station_name', 'start_lat_y', 'start_lng_y']].dropna().drop_duplicates()
station_lookup2 = df[['end_station_name', 'end_lat_y', 'end_lng_y']].dropna().drop_duplicates()
station_lookup = station_lookup.rename(columns={
    'start_station_name': 'name',
    'start_lat_y': 'lat',
    'start_lng_y': 'lng'
})

station_lookup2 = station_lookup2.rename(columns={
    'end_station_name': 'name',
    'end_lat_y': 'lat',
    'end_lng_y': 'lng'
})

combined_lookup = pd.concat([station_lookup, station_lookup2], ignore_index=True)
combined_lookup = combined_lookup.drop_duplicates(subset='name', keep='first')
df_station = df_station.merge(combined_lookup, how='left', left_on='name', right_on='name')

station_lookup = df_station[['name', 'lat', 'lng']].dropna().drop_duplicates()
gdf_station = gpd.GeoDataFrame(
    df_station,
    geometry=gpd.points_from_xy(df_station['lng'], df_station['lat']),
    crs='EPSG:4326'
)

if dc_boundary.crs != gdf_station.crs:
    dc_boundary = dc_boundary.to_crs(gdf_station.crs)

df_station['inside_dc'] = gdf_station.within(dc_boundary.unary_union)

C:\Users\asus\AppData\Local\Temp\ipykernel_12476\741496869.py:11: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.
  df_station['inside_dc'] = gdf_station.within(dc_boundary.unary_union)

df_station['inside_dc'].value_counts()

inside_dc
True     412
False    407
Name: count, dtype: int64

if Business_boundary.crs != gdf_station.crs:
    Business_boundary = Business_boundary.to_crs(gdf_station.crs)


df_station['inside_Business'] = gdf_station.within(Business_boundary.unary_union)

C:\Users\asus\AppData\Local\Temp\ipykernel_12476\2608659575.py:5: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.
  df_station['inside_Business'] = gdf_station.within(Business_boundary.unary_union)

df_station['inside_Business'].value_counts()

inside_Business
False    747
True      72
Name: count, dtype: int64

dc_boundary = dc_boundary.to_crs(epsg=4326)
Business_boundary = Business_boundary.to_crs(epsg=4326)
dc_geojson = json.loads(dc_boundary.to_json())
business_geojson = json.loads(Business_boundary.to_json())

"""
fig = px.choropleth_mapbox(
    dc_boundary,
    geojson=dc_geojson,
    locations=dc_boundary.index,  # أي عمود عادي (لأننا بس بدنا نظهر الشكل)
    color_discrete_sequence=["#888888"],  # لون حدود المدينة
    center={"lat": 38.9, "lon": -77.03},  # مركز الخريطة
    zoom=10,
    mapbox_style="carto-positron",
    opacity=0.4
)

fig2 = px.choropleth_mapbox(
    Business_boundary,
    geojson=business_geojson,
    locations=Business_boundary.index,
    color_discrete_sequence=["#FF0000"],  # لون المنطقة التجارية (أحمر)
    center={"lat": 38.9, "lon": -77.03},
    zoom=10,
    mapbox_style="carto-positron",
    opacity=0.6
)

# px لا يدعم دمج مباشر → لكن يمكننا استخدام fig.add_trace
for trace in fig2.data:
    fig.add_trace(trace)

fig.update_layout(title="حدود واشنطن مع حدود المنطقة التجارية")
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\ws.png'
image = Image.open(image_path)
display(image)

if dc_boundary.crs != "EPSG:4326":
    dc_boundary = dc_boundary.to_crs(epsg=4326)

dc_geojson = dc_boundary.__geo_interface__

geometry = [Point(xy) for xy in zip(station_lookup['lng'], station_lookup['lat'])]
gdf_stations = gpd.GeoDataFrame(station_lookup.copy(), geometry=geometry, crs="EPSG:4326")

center_lat = 38.9072
center_lon = -77.0369

"""
fig = px.choropleth_mapbox(
    dc_boundary,
    geojson=dc_geojson,
    locations=dc_boundary.index,
    color_discrete_sequence=["#cccccc"],  # لون رمادي أنعم
    center={"lat": center_lat, "lon": center_lon},
    mapbox_style="carto-positron",
    zoom=9,  # أقرب قليلاً
    opacity=0.3
)

fig.add_trace(go.Scattermapbox(
    lat=gdf_stations['lat'],
    lon=gdf_stations['lng'],
    mode='markers',
    marker=go.scattermapbox.Marker(
        size=3,
        color='red',
        opacity=0.8
    ),
    text=gdf_stations['name'],  # يظهر اسم المحطة عند المرور
    name='محطات الدراجات'
))

fig.update_layout(
    title="<b>حدود واشنطن + مواقع محطات الدراجات</b>",
    legend=dict(
        yanchor="top",
        y=0.98,
        xanchor="left",
        x=0.01
    ),
    margin={"r":0,"t":50,"l":0,"b":0},
    mapbox=dict(
        style="carto-positron",
        center=dict(lat=center_lat, lon=center_lon),
        zoom=11
    )
)
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\sl.png'
image = Image.open(image_path)
display(image)

common_columns = set(shuttle_df.columns).intersection(set(metro_df.columns))
print(" الأعمدة المشتركة:", common_columns)

if 'GIS_ID' in common_columns:
    
    shuttle_id_unique = shuttle_df['GIS_ID'].is_unique
    metro_id_unique = metro_df['GIS_ID'].is_unique
    print(f" هل 'id' فريد في shuttle؟ {shuttle_id_unique}")
    print(f" هل 'id' فريد في metro؟ {metro_id_unique}")
    
    common_ids = set(shuttle_df['GIS_ID']).intersection(set(metro_df['GIS_ID']))
    print(f" عدد القيم المتشابهة في id بين الملفين: {len(common_ids)}")
    print(" بعض القيم المتشابهة:", list(common_ids)[:10])  
else:
    print(" لا يوجد عمود مشترك اسمه 'id'")

 الأعمدة المشتركة: {'GIS_ID', 'EDITED', 'Y', 'EDITOR', 'CREATED', 'SE_ANNO_CAD_DATA', 'GLOBALID', 'X', 'OBJECTID', 'CREATOR'}
 هل 'id' فريد في shuttle؟ True
 هل 'id' فريد في metro؟ True
 عدد القيم المتشابهة في id بين الملفين: 0
 بعض القيم المتشابهة: []

shuttle_coords = set(zip(shuttle_df['LATITUDE'], shuttle_df['LONGITUDE']))
metro_coords = set(zip(metro_df['BSTP_LAT'], metro_df['BSTP_LON']))
common_coords = shuttle_coords.intersection(metro_coords)

print(f" عدد الإحداثيات المتطابقة بين Shuttle و Metro: {len(common_coords)}")
if common_coords:
    print(" أمثلة على الإحداثيات المشتركة:")
    for coord in list(common_coords)[:5]:
        print(coord)
else:
    print(" لا توجد إحداثيات متطابقة .")

 عدد الإحداثيات المتطابقة بين Shuttle و Metro: 0
 لا توجد إحداثيات متطابقة .

metro_df['full_address'] = metro_df['AT_STR'].fillna('') + ' ' + metro_df['ON_STR'].fillna('')
metro_df['full_address'] = metro_df['full_address'].str.strip().str.upper()
shuttle_df['ADDRESS_clean'] = shuttle_df['ADDRESS'].str.strip().str.upper()
metro_df['full_address']

0                 44TH ST NW JENIFER ST NW
1             HARBOUR WAY MITCHELLVILLE RD
2        SLEEPY HOLLOW MANOR COLUMBIA PIKE
3                CUSHMAN PL WAYNEWOOD BLVD
4                 PRICES LN VERNON VIEW DR
                       ...                
10039             OLD MILL RD RICHMOND HWY
10040                 FRYE RD RICHMOND HWY
10041                   #12127 OLD FORT RD
10042          BUS BAY B FT WASHINGTON LOT
10043                    MERCK PL GABLE LN
Name: full_address, Length: 10044, dtype: object

print("هل (BSTP_LAT, BSTP_LON) فريدة في Metro؟", metro_df.duplicated(subset=['BSTP_LAT', 'BSTP_LON']).any() == False)
print("هل (LATITUDE, LONGITUDE) فريدة في Shuttle؟", shuttle_df.duplicated(subset=['LATITUDE', 'LONGITUDE']).any() == False)

هل (BSTP_LAT, BSTP_LON) فريدة في Metro؟ True
هل (LATITUDE, LONGITUDE) فريدة في Shuttle؟ False

shuttle_df.drop_duplicates(subset=['LATITUDE', 'LONGITUDE'], keep='first',inplace=True)

print("هل (BSTP_LAT, BSTP_LON) فريدة في Metro؟", metro_df.duplicated(subset=['BSTP_LAT', 'BSTP_LON']).any() == False)
print("هل (LATITUDE, LONGITUDE) فريدة في Shuttle؟", shuttle_df.duplicated(subset=['LATITUDE', 'LONGITUDE']).any() == False)

هل (BSTP_LAT, BSTP_LON) فريدة في Metro؟ True
هل (LATITUDE, LONGITUDE) فريدة في Shuttle؟ True

shuttle_df = shuttle_df[['MAR_ID', 'GIS_ID', 'LATITUDE', 'LONGITUDE', 'ADDRESS']].copy()
shuttle_df.rename(columns={'MAR_ID': 'ID'}, inplace=True)
metro_df = metro_df[['EGIS_ID', 'GIS_ID', 'BSTP_LAT', 'BSTP_LON', 'full_address']].copy()
metro_df.rename(columns={'EGIS_ID': 'ID', 'BSTP_LAT': 'LATITUDE', 'BSTP_LON': 'LONGITUDE', 'full_address': 'ADDRESS'}, inplace=True)

invalid_mask = ~(
    shuttle_df['LATITUDE'].between(38, 40) & 
    shuttle_df['LONGITUDE'].between(-78, -76)
)


print(f"عدد السطور التي لا تحقق الشرط الجغرافي: {invalid_mask.sum()}")

invalid_mask = ~(
    metro_df['LATITUDE'].between(38, 40) & 
    metro_df['LONGITUDE'].between(-78, -76)
)


print(f"عدد السطور التي لا تحقق الشرط الجغرافي: {invalid_mask.sum()}")

عدد السطور التي لا تحقق الشرط الجغرافي: 0
عدد السطور التي لا تحقق الشرط الجغرافي: 0

metro_df.isna().sum()

ID           0
GIS_ID       0
LATITUDE     0
LONGITUDE    0
ADDRESS      0
dtype: int64

unique_start_coords_count = metro_df[['GIS_ID','LATITUDE', 'LONGITUDE']].drop_duplicates().dropna().shape[0]
print(f"عدد الأزواج الفريدة من خطوط الطول والعرض في بيانات ميترو: {unique_start_coords_count}")

unique_end_coords_count = shuttle_df[['GIS_ID','LATITUDE', 'LONGITUDE']].drop_duplicates().dropna().shape[0]
print(f"عدد الأزواج الفريدة من خطوط الطول والعرض في بيانات شتل: {unique_end_coords_count}")

عدد الأزواج الفريدة من خطوط الطول والعرض في بيانات ميترو: 10044
عدد الأزواج الفريدة من خطوط الطول والعرض في بيانات شتل: 96

columns_to_drop = ['X', 'Y', 'GIS_ID', 'GIS_LAST_MOD_DTTM', 'OBJECTID','IOS',
       'ANDROID','GIS_ID' ]
locations_df.drop(columns=columns_to_drop)

print(locations_df.columns)
print(f" Columns num: {len(locations_df.columns)}")
print(f"Rows num: {len(locations_df)}")

Index(['X', 'Y', 'NAME', 'STATION_TYPE', 'STATION_ID', 'STATION_STATUS',
       'LAST_REPORTED', 'NUM_DOCKS_AVAILABLE', 'NUM_DOCKS_DISABLED',
       'NUM_BIKES_AVAILABLE', 'NUM_EBIKES_AVAILABLE', 'NUM_BIKES_DISABLED',
       'IS_INSTALLED', 'IS_RETURNING', 'IS_RENTING', 'HAS_KIOSK', 'IOS',
       'ANDROID', 'ELECTRIC_BIKE_SURCHARGE_WAIVER', 'EIGHTD_HAS_KEY_DISPENSER',
       'CAPACITY', 'RENTAL_METHODS', 'REGION_ID', 'REGION_NAME', 'GIS_ID',
       'LATITUDE', 'LONGITUDE', 'GIS_LAST_MOD_DTTM', 'OBJECTID'],
      dtype='object')
 Columns num: 29
Rows num: 794

print(df_station.columns)
print(f" Columns num: {len(df_station.columns)}")
print(f"Rows num: {len(df_station)}")

Index(['id', 'name', 'lat', 'lng', 'inside_dc', 'inside_Business'], dtype='object')
 Columns num: 6
Rows num: 819

columns_to_drop = ['OBJECTID', 'ID', 'GIS_ID', 'GLOBALID', 'CREATOR', 'CREATED',
                   'EDITOR', 'EDITED', 'SHAPEAREA', 'SHAPELEN']
cbd_gdf.drop(columns=columns_to_drop, inplace=True, errors='ignore')

print(cbd_gdf.columns)
print(f" Columns num: {len(cbd_gdf.columns)}")
print(f"Rows num: {cbd_gdf.shape}")

Index(['CITY_NAME', 'STATE_CITY', 'WEB_URL', 'AREAKM', 'AREAMILES',
       'geometry'],
      dtype='object')
 Columns num: 6
Rows num: (1, 6)

columns_to_drop = ['description', 'icon', 'moonphase']
weather_df.drop(columns=columns_to_drop, inplace=True, errors='ignore')

print(locations_df.isna().sum())

X                                   0
Y                                   0
NAME                                0
STATION_TYPE                        0
STATION_ID                          0
STATION_STATUS                    794
LAST_REPORTED                       0
NUM_DOCKS_AVAILABLE                 0
NUM_DOCKS_DISABLED                  0
NUM_BIKES_AVAILABLE                 0
NUM_EBIKES_AVAILABLE                0
NUM_BIKES_DISABLED                  0
IS_INSTALLED                        0
IS_RETURNING                        0
IS_RENTING                          0
HAS_KIOSK                           0
IOS                                 0
ANDROID                             0
ELECTRIC_BIKE_SURCHARGE_WAIVER      0
EIGHTD_HAS_KEY_DISPENSER            0
CAPACITY                            0
RENTAL_METHODS                      0
REGION_ID                          50
REGION_NAME                         0
GIS_ID                            794
LATITUDE                            0
LONGITUDE                           0
GIS_LAST_MOD_DTTM                   0
OBJECTID                            0
dtype: int64

print(locations_df['STATION_TYPE'].unique())

['classic']

locations_df['STATION_TYPE'] = locations_df.apply(
    lambda row: 'classic' if row['HAS_KIOSK'] == 'YES' and row['CAPACITY'] > 0 else 'lightweight',
    axis=1
)

print(locations_df['STATION_TYPE'].unique())

['classic' 'lightweight']

status_counts = locations_df['STATION_TYPE'].value_counts()
print(status_counts)

STATION_TYPE
classic        761
lightweight     33
Name: count, dtype: int64

print(locations_df['STATION_STATUS'].unique())

[nan]

def determine_station_status(row):
    if row['IS_INSTALLED'] == 'YES' and row['IS_RENTING'] == 'YES' and row['IS_RETURNING'] == 'YES':
        return 'active'
    elif row['IS_INSTALLED'] == 'NO' or row['IS_RENTING'] == 'NO' or row['IS_RETURNING'] == 'NO':
        return 'out_of_service'
    elif pd.isna(row['IS_INSTALLED']) and pd.isna(row['IS_RENTING']) and pd.isna(row['IS_RETURNING']):
        return 'planned'
    else:
        return 'out_of_service'

locations_df['STATION_STATUS'] = locations_df.apply(determine_station_status, axis=1)

print(locations_df['STATION_STATUS'].unique())

['active' 'out_of_service']

status_counts = locations_df['STATION_STATUS'].value_counts()
print(status_counts)

STATION_STATUS
active            790
out_of_service      4
Name: count, dtype: int64

name_variants = locations_df.groupby(locations_df['NAME'].str.lower())['NAME'].nunique()
case_mismatches = name_variants[name_variants > 1]
print("Stations with same name but different casing:\n", case_mismatches)

duplicate_ids = locations_df.groupby('NAME')['STATION_ID'].nunique()
stations_with_multiple_ids = duplicate_ids[duplicate_ids > 1]
print("Stations with more than one ID:\n", stations_with_multiple_ids)

Stations with same name but different casing:
 Series([], Name: NAME, dtype: int64)
Stations with more than one ID:
 Series([], Name: STATION_ID, dtype: int64)

unique_id_count = locations_df['STATION_ID'].nunique()
print("Number of unique STATION_IDs:", unique_id_count)


unique_station_names = locations_df['NAME'].nunique()
print("Number of unique station names:", unique_station_names)

unique_station_names_case_insensitive = locations_df['NAME'].str.lower().nunique()
print("Number of unique station names (case-insensitive):", unique_station_names_case_insensitive)

Number of unique STATION_IDs: 794
Number of unique station names: 794
Number of unique station names (case-insensitive): 794

print(locations_df['NUM_DOCKS_DISABLED'].value_counts().sort_index())

NUM_DOCKS_DISABLED
0    783
1     10
7      1
Name: count, dtype: int64

locations_df['HAS_KIOSK'].value_counts()

HAS_KIOSK
YES    761
NO      33
Name: count, dtype: int64

kiosk_comparison = locations_df.groupby('HAS_KIOSK')['NUM_BIKES_AVAILABLE'].sum().reset_index()
kiosk_comparison.columns = ['Has Kiosk', 'Total Available Bikes']
print(kiosk_comparison.to_string(index=False))

Has Kiosk  Total Available Bikes
       NO                    290
      YES                   4807

kiosk_comparison_df = locations_df[['NAME', 'HAS_KIOSK', 'NUM_BIKES_AVAILABLE','CAPACITY']]
display(kiosk_comparison_df.sort_values(by='HAS_KIOSK'))

capacity_mismatch = locations_df[
    locations_df['CAPACITY'] != (locations_df['NUM_DOCKS_AVAILABLE'] + locations_df['NUM_DOCKS_DISABLED'])
]

print("Number of mismatches:", len(capacity_mismatch))

Number of mismatches: 736

capacity_mismatch[['NAME', 'CAPACITY', 'NUM_DOCKS_AVAILABLE', 'NUM_DOCKS_DISABLED']]

es = locations_df[locations_df['NUM_EBIKES_AVAILABLE'] > locations_df['NUM_BIKES_AVAILABLE']]
print("Stations where electric bikes > total bikes:", len(invalid_ebikes))
invalid_docks = locations_df[
    (locations_df['NUM_DOCKS_AVAILABLE'] + locations_df['NUM_DOCKS_DISABLED']) > locations_df['CAPACITY']
]
print("Stations where available + disabled docks > capacity:", len(invalid_docks))

not_installed = locations_df[locations_df['IS_INSTALLED'] == False]
violating_not_installed = not_installed[
    (not_installed[['NUM_BIKES_AVAILABLE', 'NUM_EBIKES_AVAILABLE', 'NUM_BIKES_DISABLED',
                    'NUM_DOCKS_AVAILABLE', 'NUM_DOCKS_DISABLED']] != 0).any(axis=1) |
    (not_installed[['IS_RENTING', 'IS_RETURNING']] != False).any(axis=1)
]
print("Stations marked not installed but still active:", len(violating_not_installed))

invalid_returning = locations_df[
    (locations_df['IS_RETURNING'] == False) & (locations_df['NUM_DOCKS_AVAILABLE'] > 0)
]
print("Stations not accepting returns but reporting available docks:", len(invalid_returning))

invalid_renting = locations_df[
    (locations_df['IS_RENTING'] == False) &
    ((locations_df['NUM_BIKES_AVAILABLE'] > 0) | (locations_df['NUM_EBIKES_AVAILABLE'] > 0))
]
print("Stations not renting but reporting available bikes:", len(invalid_renting))


invalid_kiosk = locations_df[
    (locations_df['HAS_KIOSK'] == False) & (locations_df['STATION_TYPE'] == 'classic')
]
print("Classic stations marked without a kiosk :", len(invalid_kiosk))

Stations where electric bikes > total bikes: 0
Stations where available + disabled docks > capacity: 0
Stations marked not installed but still active: 0
Stations not accepting returns but reporting available docks: 0
Stations not renting but reporting available bikes: 0
Classic stations marked without a kiosk : 0

print(weather_df.columns)
print(f"Columns num : {len(weather_df.columns)}")
print(f"Rows num: {len(weather_df)}")

Index(['name', 'datetime', 'tempmax', 'tempmin', 'temp', 'humidity',
       'windspeed', 'windspeedmax', 'windspeedmean', 'windspeedmin',
       'cloudcover', 'sunrise', 'sunset', 'conditions', 'temp_range',
       'weather_bin', 'date'],
      dtype='object')
Columns num : 17
Rows num: 366

weather_df.dtypes

name              object
datetime          object
tempmax          float64
tempmin          float64
temp             float64
humidity         float64
windspeed        float64
windspeedmax     float64
windspeedmean    float64
windspeedmin     float64
cloudcover       float64
sunrise           object
sunset            object
conditions        object
temp_range        object
weather_bin       object
date              object
dtype: object

weather_df['datetime'] = pd.to_datetime(weather_df['datetime'], errors='coerce')
weather_df['sunrise'] = pd.to_datetime(weather_df['sunrise'], errors='coerce')
weather_df['sunset'] = pd.to_datetime(weather_df['sunset'], errors='coerce')

print(weather_df.dtypes)

name                     object
datetime         datetime64[ns]
tempmax                 float64
tempmin                 float64
temp                    float64
humidity                float64
windspeed               float64
windspeedmax            float64
windspeedmean           float64
windspeedmin            float64
cloudcover              float64
sunrise          datetime64[ns]
sunset           datetime64[ns]
conditions               object
temp_range               object
weather_bin              object
date                     object
dtype: object

weather_df = weather_df.sort_values('datetime')

duplicates = weather_df['datetime'].duplicated().sum()
print(f"Number of duplicated dates: {duplicates}")

full_range = pd.date_range(start=weather_df['datetime'].min(), end=weather_df['datetime'].max(), freq='D')
missing_dates = full_range.difference(weather_df['datetime'])
print(f"Missing dates:\n{missing_dates}")

Number of duplicated dates: 0
Missing dates:
DatetimeIndex([], dtype='datetime64[ns]', freq='D')

weather_df['datetime'] = pd.to_datetime(weather_df['datetime'], errors='coerce')

print(weather_df['datetime'].min(), "to", weather_df['datetime'].max())
print(weather_df['datetime'].dt.date.value_counts().sort_index())

2024-01-01 00:00:00 to 2024-12-31 00:00:00
datetime
2024-01-01    1
2024-01-02    1
2024-01-03    1
2024-01-04    1
2024-01-05    1
             ..
2024-12-27    1
2024-12-28    1
2024-12-29    1
2024-12-30    1
2024-12-31    1
Name: count, Length: 366, dtype: int64

weather_counts = weather_df['conditions'].value_counts()

print("All weather conditions and their day counts:")
print(weather_counts)

All weather conditions and their day counts:
conditions
Partially cloudy                197
Rain, Partially cloudy           74
Rain, Overcast                   59
Clear                            24
Overcast                          6
Snow, Rain, Overcast              3
Snow, Rain, Partially cloudy      2
Snow, Partially cloudy            1
Name: count, dtype: int64

rainy_days = weather_df[weather_df['conditions'].str.contains("Rain", case=False, na=False)]
sunny_days = weather_df[weather_df['conditions'].str.contains("Clear", case=False, na=False)]

print("All rainy days:")
print(rainy_days[['datetime', 'conditions']].to_string(index=False))

print("\nAll sunny days:")
print(sunny_days[['datetime', 'conditions']].to_string(index=False))

All rainy days:
  datetime                   conditions
2024-01-01               Rain, Overcast
2024-01-06               Rain, Overcast
2024-01-09               Rain, Overcast
2024-01-10       Rain, Partially cloudy
2024-01-12       Rain, Partially cloudy
2024-01-13       Rain, Partially cloudy
2024-01-15         Snow, Rain, Overcast
2024-01-16 Snow, Rain, Partially cloudy
2024-01-19         Snow, Rain, Overcast
2024-01-25               Rain, Overcast
2024-01-26       Rain, Partially cloudy
2024-01-27       Rain, Partially cloudy
2024-01-28               Rain, Overcast
2024-02-02               Rain, Overcast
2024-02-11               Rain, Overcast
2024-02-12               Rain, Overcast
2024-02-13       Rain, Partially cloudy
2024-02-17 Snow, Rain, Partially cloudy
2024-02-23       Rain, Partially cloudy
2024-02-24       Rain, Partially cloudy
2024-02-27       Rain, Partially cloudy
2024-02-28               Rain, Overcast
2024-03-01       Rain, Partially cloudy
2024-03-02               Rain, Overcast
2024-03-04               Rain, Overcast
2024-03-05       Rain, Partially cloudy
2024-03-06               Rain, Overcast
2024-03-07       Rain, Partially cloudy
2024-03-09               Rain, Overcast
2024-03-10       Rain, Partially cloudy
2024-03-15       Rain, Partially cloudy
2024-03-22       Rain, Partially cloudy
2024-03-23               Rain, Overcast
2024-03-27               Rain, Overcast
2024-03-28               Rain, Overcast
2024-03-31       Rain, Partially cloudy
2024-04-01               Rain, Overcast
2024-04-02               Rain, Overcast
2024-04-03               Rain, Overcast
2024-04-04       Rain, Partially cloudy
2024-04-11               Rain, Overcast
2024-04-12       Rain, Partially cloudy
2024-04-15       Rain, Partially cloudy
2024-04-19               Rain, Overcast
2024-04-20       Rain, Partially cloudy
2024-04-27               Rain, Overcast
2024-05-01       Rain, Partially cloudy
2024-05-04               Rain, Overcast
2024-05-05               Rain, Overcast
2024-05-06               Rain, Overcast
2024-05-07       Rain, Partially cloudy
2024-05-09       Rain, Partially cloudy
2024-05-10               Rain, Overcast
2024-05-11       Rain, Partially cloudy
2024-05-12       Rain, Partially cloudy
2024-05-14               Rain, Overcast
2024-05-15               Rain, Overcast
2024-05-18               Rain, Overcast
2024-05-23       Rain, Partially cloudy
2024-05-24       Rain, Partially cloudy
2024-05-25       Rain, Partially cloudy
2024-05-26       Rain, Partially cloudy
2024-05-27       Rain, Partially cloudy
2024-05-28       Rain, Partially cloudy
2024-05-29       Rain, Partially cloudy
2024-05-30       Rain, Partially cloudy
2024-06-03       Rain, Partially cloudy
2024-06-05               Rain, Overcast
2024-06-10       Rain, Partially cloudy
2024-06-11       Rain, Partially cloudy
2024-06-14       Rain, Partially cloudy
2024-06-26       Rain, Partially cloudy
2024-06-27       Rain, Partially cloudy
2024-06-30       Rain, Partially cloudy
2024-07-04       Rain, Partially cloudy
2024-07-10       Rain, Partially cloudy
2024-07-12               Rain, Overcast
2024-07-13       Rain, Partially cloudy
2024-07-17       Rain, Partially cloudy
2024-07-20       Rain, Partially cloudy
2024-07-21       Rain, Partially cloudy
2024-07-22               Rain, Overcast
2024-07-23       Rain, Partially cloudy
2024-07-24               Rain, Overcast
2024-07-25       Rain, Partially cloudy
2024-07-29       Rain, Partially cloudy
2024-07-30               Rain, Overcast
2024-07-31       Rain, Partially cloudy
2024-08-03       Rain, Partially cloudy
2024-08-07       Rain, Partially cloudy
2024-08-08               Rain, Overcast
2024-08-09               Rain, Overcast
2024-08-10       Rain, Partially cloudy
2024-08-17       Rain, Partially cloudy
2024-08-19       Rain, Partially cloudy
2024-08-26       Rain, Partially cloudy
2024-08-29       Rain, Partially cloudy
2024-08-30               Rain, Overcast
2024-08-31               Rain, Overcast
2024-09-01       Rain, Partially cloudy
2024-09-04       Rain, Partially cloudy
2024-09-07       Rain, Partially cloudy
2024-09-17               Rain, Overcast
2024-09-18               Rain, Overcast
2024-09-22               Rain, Overcast
2024-09-23               Rain, Overcast
2024-09-24               Rain, Overcast
2024-09-25               Rain, Overcast
2024-09-26               Rain, Overcast
2024-09-27               Rain, Overcast
2024-09-28       Rain, Partially cloudy
2024-09-29               Rain, Overcast
2024-09-30               Rain, Overcast
2024-10-01               Rain, Overcast
2024-10-02               Rain, Overcast
2024-10-03       Rain, Partially cloudy
2024-11-10       Rain, Partially cloudy
2024-11-11       Rain, Partially cloudy
2024-11-14               Rain, Overcast
2024-11-15       Rain, Partially cloudy
2024-11-19       Rain, Partially cloudy
2024-11-20               Rain, Overcast
2024-11-21       Rain, Partially cloudy
2024-11-22       Rain, Partially cloudy
2024-11-26       Rain, Partially cloudy
2024-11-28       Rain, Partially cloudy
2024-12-09       Rain, Partially cloudy
2024-12-10               Rain, Overcast
2024-12-11               Rain, Overcast
2024-12-15               Rain, Overcast
2024-12-16               Rain, Overcast
2024-12-18       Rain, Partially cloudy
2024-12-19       Rain, Partially cloudy
2024-12-20               Rain, Overcast
2024-12-24         Snow, Rain, Overcast
2024-12-28               Rain, Overcast
2024-12-29               Rain, Overcast
2024-12-31       Rain, Partially cloudy

All sunny days:
  datetime conditions
2024-01-17      Clear
2024-01-21      Clear
2024-02-03      Clear
2024-02-04      Clear
2024-02-06      Clear
2024-02-19      Clear
2024-02-29      Clear
2024-03-24      Clear
2024-03-25      Clear
2024-05-31      Clear
2024-06-22      Clear
2024-10-06      Clear
2024-10-08      Clear
2024-10-09      Clear
2024-10-11      Clear
2024-10-18      Clear
2024-10-19      Clear
2024-10-20      Clear
2024-10-21      Clear
2024-10-22      Clear
2024-10-23      Clear
2024-10-25      Clear
2024-12-02      Clear
2024-12-03      Clear

max_temp = weather_df['tempmax'].max()
max_temp_days = weather_df[weather_df['tempmax'] == max_temp]

print(f" Highest temperature: {max_temp}°C")
print(f"Occurred on {len(max_temp_days)} day(s) with these conditions:")
print(max_temp_days[['datetime', 'tempmax', 'conditions']].to_string(index=False))

min_temp = weather_df['tempmin'].min()
min_temp_days = weather_df[weather_df['tempmin'] == min_temp]

print(f"\n Lowest temperature: {min_temp}°C")
print(f"Occurred on {len(min_temp_days)} day(s) with these conditions:")
print(min_temp_days[['datetime', 'tempmin', 'conditions']].to_string(index=False))

 Highest temperature: 39.3°C
Occurred on 1 day(s) with these conditions:
  datetime  tempmax       conditions
2024-07-16     39.3 Partially cloudy

 Lowest temperature: -9.0°C
Occurred on 1 day(s) with these conditions:
  datetime  tempmin conditions
2024-01-17     -9.0      Clear

max_humidity = weather_df['humidity'].max()
humid_days = weather_df[weather_df['humidity'] == max_humidity]
print(f"Highest humidity: {max_humidity}%")
print("Occurred on:")
print(humid_days[['datetime', 'humidity', 'conditions']])

Highest humidity: 91.7%
Occurred on:
      datetime  humidity      conditions
344 2024-12-10      91.7  Rain, Overcast

max_wind = weather_df['windspeed'].max()
windiest_days = weather_df[weather_df['windspeed'] == max_wind]
print(f"Highest wind speed: {max_wind} km/h")
print("Occurred on:")
print(windiest_days[['datetime', 'windspeed', 'conditions']])

Highest wind speed: 50.8 km/h
Occurred on:
      datetime  windspeed        conditions
339 2024-12-05       50.8  Partially cloudy

mixed_days = weather_df[
    weather_df['conditions'].str.contains("Rain", case=False, na=False) & 
    weather_df['conditions'].str.contains("Snow", case=False, na=False)
]

print(" Days with both Rain and Snow:")
print(mixed_days[['datetime', 'conditions', 'tempmin', 'tempmax', 'temp']])

 Days with both Rain and Snow:
      datetime                    conditions  tempmin  tempmax  temp
14  2024-01-15          Snow, Rain, Overcast     -3.4     -0.7  -1.9
15  2024-01-16  Snow, Rain, Partially cloudy     -6.7     -0.7  -2.1
18  2024-01-19          Snow, Rain, Overcast     -1.3      0.7  -0.3
47  2024-02-17  Snow, Rain, Partially cloudy     -0.1      4.9   2.4
358 2024-12-24          Snow, Rain, Overcast     -1.3      4.3   1.8

bins = [-10, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
labels = ['<0°C', '0–5°C', '5–10°C', '10–15°C', '15–20°C', '20–25°C', '25–30°C', '30–35°C', '35–40°C', '40+°C']
weather_df['temp_range'] = pd.cut(weather_df['temp'], bins=bins, labels=labels)

temp_counts = weather_df['temp_range'].value_counts().sort_index()

print("\n Most Frequent Average Temperature Ranges:")
print(temp_counts)

 Most Frequent Average Temperature Ranges:
temp_range
<0°C       10
0–5°C      36
5–10°C     56
10–15°C    70
15–20°C    48
20–25°C    69
25–30°C    64
30–35°C    13
35–40°C     0
40+°C       0
Name: count, dtype: int64

combo_counts = weather_df.groupby(['temp_range', 'conditions']).size().sort_values(ascending=False)

print("\n Most Common Condition + Temp Range Combinations:")
print(combo_counts)

 Most Common Condition + Temp Range Combinations:
temp_range  conditions                  
20–25°C     Partially cloudy                47
10–15°C     Partially cloudy                39
25–30°C     Partially cloudy                32
            Rain, Partially cloudy          26
0–5°C       Partially cloudy                24
                                            ..
20–25°C     Snow, Rain, Overcast             0
            Snow, Rain, Partially cloudy     0
25–30°C     Clear                            0
            Overcast                         0
40+°C       Snow, Rain, Partially cloudy     0
Length: 80, dtype: int64

weather_df['datetime'] = pd.to_datetime(weather_df['datetime'])
"""
fig = px.line(
    weather_df,
    x='datetime',
    y=['temp', 'humidity'],
    labels={'value': 'Measurement', 'variable': 'Metric'},
    title='Daily Temperature and Humidity in 2024'
)
fig.show()
"""

image_path = r'C:\Users\ASUS\OneDrive\Desktop\106.png'
image = Image.open(image_path)
display(image)

weather_df['datetime'] = pd.to_datetime(weather_df['datetime'])
time_series_metrics = [
    'temp',          
    'humidity',       
    'tempmax',        
    'tempmin',       
    'windspeed',     
    'cloudcover'      
]
"""

fig = px.line(
    weather_df,
    x='datetime',
    y=time_series_metrics,
    labels={'value': 'Measurement', 'variable': 'Metric'},
    title='Daily Weather Metrics in 2024'
)
fig.show()
"""

image_path = r'C:\Users\ASUS\OneDrive\Desktop\107.png'
image = Image.open(image_path)
display(image)

parking_zones_gdf = parking_zones_gdf.drop(columns=['SHAPELEN', 'SHAPEAREA', 'WEB_URL', 'GIS_ID','OBJECTID','SHAPE_LENG'])
parking_zones_gdf = parking_zones_gdf.drop(columns=['CREATOR', 'CREATED', 'EDITOR', 'EDITED'])

print(parking_zones_gdf.columns)
print(parking_zones_gdf.head())
print(f"Columns num : {len(parking_zones_gdf.columns)}")
print(f"Rows num: {len(parking_zones_gdf)}")

Index(['NAME', 'RPP_ZONE', 'ANC_ID', 'geometry'], dtype='object')
     NAME  RPP_ZONE ANC_ID                                           geometry
0  ANC 4A         4     4A  POLYGON ((-77.03331 38.98999, -77.03332 38.99,...
1  ANC 3G         3     3G  POLYGON ((-77.05161 38.98615, -77.05181 38.986...
2  ANC 4B         4     4B  POLYGON ((-76.99908 38.96328, -76.99909 38.963...
3  ANC 3F         3     3F  POLYGON ((-77.04702 38.96038, -77.04722 38.960...
4  ANC 4C         4     4C  POLYGON ((-77.02803 38.95622, -77.02804 38.956...
Columns num : 4
Rows num: 40

columns_to_check = [
    'NAME', 'RPP_ZONE', 'ANC_ID', 'geometry'
]

for col in columns_to_check:
    print(f"\nUnique values in '{col}':")
    print(parking_zones_gdf[col].unique())

Unique values in 'NAME':
['ANC 4A' 'ANC 3G' 'ANC 4B' 'ANC 3F' 'ANC 4C' 'ANC 4D' 'ANC 5A' 'ANC 5B'
 'ANC 3D' 'ANC 3C' 'ANC 1D' 'ANC 1A' 'ANC 5E' 'ANC 3B' 'ANC 1B' 'ANC 1C'
 'ANC 2D' 'ANC 2E' 'ANC 5D' 'ANC 2B' 'ANC 6E' 'ANC 2F' 'ANC 7C' 'ANC 6C'
 'ANC 2A' 'ANC 6A' 'ANC 2C' 'ANC 7F' 'ANC 6B' 'ANC 7E' 'ANC 6D' 'ANC 7B'
 'ANC 8A' 'ANC 8C' 'ANC 8B' 'ANC 8E' 'ANC 8D' 'ANC 3E' 'ANC 5C' 'ANC 7D']

Unique values in 'RPP_ZONE':
[4 3 5 1 2 6 7 8]

Unique values in 'ANC_ID':
['4A' '3G' '4B' '3F' '4C' '4D' '5A' '5B' '3D' '3C' '1D' '1A' '5E' '3B'
 '1B' '1C' '2D' '2E' '5D' '2B' '6E' '2F' '7C' '6C' '2A' '6A' '2C' '7F'
 '6B' '7E' '6D' '7B' '8A' '8C' '8B' '8E' '8D' '3E' '5C' '7D']

Unique values in 'geometry':
<GeometryArray>
[<POLYGON ((-77.033 38.99, -77.033 38.99, -77.041 38.996, -77.052 38.987, -77...>,
 <POLYGON ((-77.052 38.986, -77.052 38.986, -77.052 38.986, -77.052 38.986, -...>,
 <POLYGON ((-76.999 38.963, -76.999 38.963, -77.002 38.966, -77.002 38.966, -...>,
 <POLYGON ((-77.047 38.96, -77.047 38.96, -77.047 38.96, -77.048 38.96, -77.0...>,
 <POLYGON ((-77.028 38.956, -77.028 38.956, -77.028 38.957, -77.028 38.957, -...>,
 <POLYGON ((-77.019 38.958, -77.019 38.958, -77.019 38.958, -77.019 38.958, -...>,
 <POLYGON ((-76.991 38.957, -76.991 38.957, -76.992 38.957, -76.992 38.957, -...>,
 <POLYGON ((-76.982 38.95, -76.982 38.95, -76.983 38.95, -76.983 38.95, -76.9...>,
 <POLYGON ((-77.101 38.949, -77.101 38.949, -77.101 38.949, -77.101 38.949, -...>,
 <POLYGON ((-77.078 38.943, -77.078 38.943, -77.078 38.943, -77.078 38.943, -...>,
 <POLYGON ((-77.036 38.936, -77.036 38.937, -77.036 38.937, -77.036 38.937, -...>,
 <POLYGON ((-77.035 38.937, -77.036 38.937, -77.036 38.937, -77.036 38.937, -...>,
 <POLYGON ((-76.994 38.927, -76.994 38.928, -76.994 38.928, -76.994 38.929, -...>,
 <POLYGON ((-77.077 38.928, -77.077 38.928, -77.077 38.928, -77.077 38.928, -...>,
 <POLYGON ((-77.019 38.929, -77.019 38.929, -77.019 38.929, -77.019 38.929, -...>,
 <POLYGON ((-77.046 38.926, -77.047 38.926, -77.047 38.926, -77.047 38.926, -...>,
 <POLYGON ((-77.049 38.92, -77.05 38.92, -77.05 38.921, -77.05 38.921, -77.05...>,
 <POLYGON ((-77.067 38.919, -77.067 38.918, -77.067 38.918, -77.067 38.918, -...>,
 <POLYGON ((-76.98 38.908, -76.98 38.908, -76.98 38.908, -76.98 38.908, -76.9...>,
 <POLYGON ((-77.041 38.917, -77.041 38.917, -77.041 38.917, -77.041 38.917, -...>,
 <POLYGON ((-77.018 38.914, -77.018 38.914, -77.018 38.915, -77.018 38.915, -...>,
 <POLYGON ((-77.032 38.914, -77.032 38.914, -77.032 38.914, -77.033 38.914, -...>,
 <POLYGON ((-76.931 38.91, -76.931 38.91, -76.931 38.91, -76.931 38.91, -76.9...>,
 <POLYGON ((-76.995 38.901, -76.995 38.901, -76.995 38.902, -76.995 38.902, -...>,
 <POLYGON ((-77.054 38.907, -77.054 38.907, -77.055 38.907, -77.055 38.907, -...>,
 <POLYGON ((-76.987 38.901, -76.987 38.901, -76.987 38.902, -76.987 38.902, -...>,
 <POLYGON ((-77.024 38.903, -77.024 38.903, -77.024 38.903, -77.025 38.903, -...>,
 <POLYGON ((-76.947 38.898, -76.949 38.898, -76.949 38.898, -76.949 38.898, -...>,
 <POLYGON ((-77.005 38.89, -77.006 38.89, -77.006 38.89, -77.009 38.89, -77.0...>,
 <POLYGON ((-76.936 38.89, -76.936 38.89, -76.936 38.89, -76.936 38.89, -76.9...>,
 <POLYGON ((-77.01 38.888, -77.011 38.888, -77.011 38.888, -77.011 38.888, -7...>,
 <POLYGON ((-76.965 38.887, -76.965 38.887, -76.965 38.886, -76.965 38.886, -...>,
 <POLYGON ((-76.972 38.873, -76.972 38.873, -76.972 38.873, -76.973 38.873, -...>,
 <POLYGON ((-76.993 38.858, -76.993 38.859, -76.993 38.859, -76.993 38.859, -...>,
 <POLYGON ((-76.973 38.863, -76.973 38.863, -76.974 38.863, -76.974 38.864, -...>,
 <POLYGON ((-76.975 38.853, -76.975 38.853, -76.975 38.853, -76.975 38.853, -...>,
 <POLYGON ((-77.022 38.841, -77.022 38.841, -77.022 38.841, -77.033 38.844, -...>,
 <POLYGON ((-77.072 38.951, -77.072 38.951, -77.072 38.951, -77.072 38.951, -...>,
 <POLYGON ((-76.971 38.938, -76.971 38.938, -76.971 38.938, -76.971 38.938, -...>,
 <POLYGON ((-76.937 38.906, -76.937 38.906, -76.937 38.906, -76.936 38.906, -...>]
Length: 40, dtype: geometry

unique_names = parking_zones_gdf['NAME'].nunique()
print(f"Number of parking zones in 'NAME': {unique_names}")

unique_names = parking_zones_gdf['RPP_ZONE'].nunique()
print(f"Number of RPP ZONE: {unique_names}")

unique_names = parking_zones_gdf['ANC_ID'].nunique()
print(f"Number of ANC_ID: {unique_names}")

unique_names = parking_zones_gdf['geometry'].nunique()
print(f"Number of geometry: {unique_names}")

Number of parking zones in 'NAME': 40
Number of RPP ZONE: 8
Number of ANC_ID: 40
Number of geometry: 40

center = parking_zones_gdf.geometry.centroid.unary_union.centroid
map_center = [center.y, center.x]
"""
m = folium.Map(location=map_center, zoom_start=12)
GeoJson(parking_zones_gdf).add_to(m)
GeoJson(
    parking_zones_gdf,
    tooltip=folium.GeoJsonTooltip(fields=["NAME", "RPP_ZONE"])
).add_to(m)

m
"""

image_path = r'C:\Users\ASUS\OneDrive\Desktop\113.png'
image = Image.open(image_path)
display(image)

C:\Users\ASUS\AppData\Local\Temp\ipykernel_2628\2410221009.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  center = parking_zones_gdf.geometry.centroid.unary_union.centroid
C:\Users\ASUS\AppData\Local\Temp\ipykernel_2628\2410221009.py:2: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.
  center = parking_zones_gdf.geometry.centroid.unary_union.centroid

locations_df['geometry'] = locations_df.apply(lambda row: Point(row['LONGITUDE'], row['LATITUDE']), axis=1)
locations_gdf = gpd.GeoDataFrame(locations_df, geometry='geometry', crs='EPSG:4326')

arking_zones_gdf = parking_zones_gdf.to_crs(epsg=4326)
locations_gdf = locations_gdf.to_crs(epsg=4326)

stations_with_parking_zone = gpd.sjoin(
    locations_gdf,
    parking_zones_gdf[['NAME', 'geometry']].copy(),
    how='left',
    predicate='within'
)

stations_with_parking_zone.rename(columns={'NAME_right': 'ZONE_NAME'}, inplace=True)
stations_with_parking_zone['ZONE_NAME'].value_counts()

ZONE_NAME
ANC 2A    34
ANC 6D    31
ANC 2C    28
ANC 2B    24
ANC 6C    21
ANC 1B    18
ANC 5E    17
ANC 6B    16
ANC 2F    13
ANC 6A    12
ANC 5D    11
ANC 8A    11
ANC 1C    11
ANC 2E    11
ANC 1A    10
ANC 6E     9
ANC 3D     9
ANC 3C     9
ANC 4C     8
ANC 5B     8
ANC 5C     8
ANC 7D     8
ANC 7B     7
ANC 4B     6
ANC 5A     6
ANC 3E     6
ANC 4A     5
ANC 8D     5
ANC 7C     5
ANC 4D     5
ANC 3F     4
ANC 8B     4
ANC 8E     4
ANC 7E     4
ANC 7F     4
ANC 3B     3
ANC 8C     3
ANC 3G     3
ANC 1D     2
Name: count, dtype: int64

stations_with_parking_zone['ZONE_NAME'].isna().sum()

391

outside_parking_zones = stations_with_parking_zone[stations_with_parking_zone['ZONE_NAME'].isna()]
outside_parking_zones[['NAME_left', 'LATITUDE', 'LONGITUDE', 'STATION_STATUS']]

outside_parking_zones['STATION_STATUS'].value_counts()

STATION_STATUS
active            390
out_of_service      1
Name: count, dtype: int64

(outside_parking_zones['NUM_BIKES_AVAILABLE'] == 0).sum()

38

"""
dc_boundary = dc_boundary.to_crs(epsg=4326)
map_center = outside_parking_zones.geometry.unary_union.centroi



m = folium.Map(location=[map_center.y, map_center.x], zoom_start=12)
tooltip_fields = [col for col in dc_boundary.columns if col != 'geometry']
folium.GeoJson(
    dc_boundary,
    name="DC Boundary",
    style_function=lambda feature: {
        'fillColor': 'none',
        'color': 'blue',
        'weight': 2,
        'dashArray': '5, 5'
    },
    tooltip=folium.GeoJsonTooltip(fields=tooltip_fields)
).add_to(m)
for _, row in outside_parking_zones.iterrows():
    color = 'red' if row['STATION_STATUS'] == 'out_of_service' else 'green'
    folium.CircleMarker(
        location=[row['LATITUDE'], row['LONGITUDE']],
        radius=5,
        color=color,
        fill=True,
        fill_opacity=0.7,
        popup=folium.Popup(f"
            <b>{row['NAME_left']}</b><br>
            Status: {row['STATION_STATUS']}<br>
            No Zone
        ", max_width=250)
    ).add_to(m)

m
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\123.png'
image = Image.open(image_path)
display(image)

stations_with_parking_zone.groupby(['ZONE_NAME', 'STATION_TYPE']).size().unstack(fill_value=0)

outside_parking_zones['STATION_TYPE'].value_counts()

STATION_TYPE
classic        378
lightweight     13
Name: count, dtype: int64

stations_with_parking_zone.groupby(['ZONE_NAME', 'STATION_STATUS']).size().unstack(fill_value=0)

all_parking_zone_names = set(parking_zones_gdf['NAME'].unique())
zones_with_bikes = set(stations_with_parking_zone['ZONE_NAME'].dropna().unique())
zones_without_bikes = all_parking_zone_names - zones_with_bikes
print("zones_without_bikes:")
print(zones_without_bikes)

zones_without_bikes:
{'ANC 2D'}

# dc_boundary = dc_boundary.to_crs(epsg=4326)
# center = dc_boundary.geometry.unary_union.centroid
# m = folium.Map(location=[center.y, center.x], zoom_start=12)
"""
tooltip_fields = [col for col in dc_boundary.columns if col != 'geometry']
folium.GeoJson(
    dc_boundary,
    name="DC Boundary",
    style_function=lambda feature: {
        'fillColor': 'none',
        'color': 'blue',
        'weight': 2,
        'dashArray': '5, 5'
    },
    tooltip=folium.GeoJsonTooltip(fields=tooltip_fields)
).add_to(m)
stations_in_zones = stations_with_parking_zone[stations_with_parking_zone['ZONE_NAME'].notna()]
for _, row in stations_in_zones.iterrows():
    color = 'red' if row['STATION_STATUS'] == 'out_of_service' else 'green'
    folium.CircleMarker(
        location=[row['LATITUDE'], row['LONGITUDE']],
        radius=5,
        color=color,
        fill=True,
        fill_opacity=0.7,
        popup=folium.Popup(f""
            <b>{row['NAME_left']}</b><br>
            Status: {row['STATION_STATUS']}<br>
            Zone: {row['ZONE_NAME']}
        "", max_width=250)
    ).add_to(m)

m
"""

image_path = r"C:\Users\asus\Desktop\photo_2025-06-22_01-05-41.jpg"
image = Image.open(image_path)
display(image)

map_center = cbd_gdf.geometry.unary_union.centroid
m = folium.Map(location=[map_center.y, map_center.x], zoom_start=13)
"""
folium.GeoJson(
    cbd_gdf,
    name="CBD",
    style_function=lambda feature: {
        'fillColor': 'red',
        'color': 'darkred',
        'weight': 2,
        'fillOpacity': 0.2
    },
    tooltip="Central Business District (ممنوع ركوب الدراجة على الأرصفة)"
).add_to(m)


folium.GeoJson(
    parking_zones_gdf,
    name="Parking Zones",
    style_function=lambda feature: {
        'fillColor': 'blue',
        'color': 'blue',
        'weight': 1,
        'fillOpacity': 0.1
    },
    tooltip=folium.GeoJsonTooltip(fields=['NAME'])
).add_to(m)

folium.LayerControl().add_to(m)
m
"""

image_path = r'C:\Users\ASUS\OneDrive\Desktop\129.png'
image = Image.open(image_path)
display(image)

C:\Users\ASUS\AppData\Local\Temp\ipykernel_14696\3232377439.py:1: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.
  map_center = cbd_gdf.geometry.unary_union.centroid

map_center = cbd_gdf.geometry.unary_union.centroid
"""
m = folium.Map(location=[map_center.y, map_center.x], zoom_start=13)
folium.GeoJson(
    cbd_gdf,
    name="CBD",
    style_function=lambda feature: {
        'fillColor': 'red',
        'color': 'darkred',
        'weight': 2,
        'fillOpacity': 0.2
    },
    tooltip=" Central Business District - لا يسمح بركوب الدراجة على الأرصفة"
).add_to(m)

folium.GeoJson(
    parking_zones_gdf,
    name="Parking Zones",
    style_function=lambda feature: {
        'fillColor': 'blue',
        'color': 'blue',
        'weight': 1,
        'fillOpacity': 0.1
    },
    tooltip=folium.GeoJsonTooltip(fields=['NAME'])
).add_to(m)

for _, row in stations_with_parking_zone.iterrows():
    color = 'red' if row['STATION_STATUS'] == 'out_of_service' else 'green'
    popup_text = f""
        <b>{row['NAME_left']}</b><br>
        Status: {row['STATION_STATUS']}<br>
        Zone: {row['ZONE_NAME'] if pd.notna(row['ZONE_NAME']) else 'No Zone'}
    ""
    folium.CircleMarker(
        location=[row['LATITUDE'], row['LONGITUDE']],
        radius=5,
        color=color,
        fill=True,
        fill_opacity=0.7,
        popup=folium.Popup(popup_text, max_width=250)
    ).add_to(m)

folium.LayerControl().add_to(m)

m
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\130.png'
image = Image.open(image_path)
display(image)

C:\Users\ASUS\AppData\Local\Temp\ipykernel_2628\3368861370.py:1: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.
  map_center = cbd_gdf.geometry.unary_union.centroid

parking_zones_in_cbd = parking_zones_gdf[parking_zones_gdf.intersects(cbd_gdf.unary_union)].copy()

C:\Users\asus\AppData\Local\Temp\ipykernel_12476\2778132266.py:3: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.
  parking_zones_in_cbd = parking_zones_gdf[parking_zones_gdf.intersects(cbd_gdf.unary_union)].copy()

print(f"parking zones in cbd: {len(parking_zones_in_cbd)}")

parking zones in cbd: 40

parking_zones_in_cbd.head()

def add_time_features_fast(df):
    started = df['started_at']
    ended = df['ended_at']
    
    start_year = started.dt.year
    end_year = ended.dt.year

    start_month = started.dt.month
    end_month = ended.dt.month

    start_day = started.dt.day
    end_day = ended.dt.day

    start_day_name = started.dt.day_name()
    end_day_name = ended.dt.day_name()
    start_hour_24 = started.dt.hour
    start_hour_12 = ((start_hour_24 + 11) % 12) + 1
    start_am_pm = np.where(start_hour_24 < 12, 'AM', 'PM')
    start_hour_am_pm = start_hour_12.astype(str) + ' ' + start_am_pm

    end_hour_24 = ended.dt.hour
    end_hour_12 = ((end_hour_24 + 11) % 12) + 1
    end_am_pm = np.where(end_hour_24 < 12, 'AM', 'PM')
    end_hour_am_pm = end_hour_12.astype(str) + ' ' + end_am_pm

    ride_duration_min = ((ended - started).dt.total_seconds() / 60).round(1)

    return df.assign(
        start_year=start_year,
        end_year=end_year,
        start_month=start_month,
        end_month=end_month,
        start_day=start_day,
        end_day=end_day,
        start_day_name=start_day_name,
        end_day_name=end_day_name,
        start_hour_am_pm=start_hour_am_pm,
        end_hour_am_pm=end_hour_am_pm,
        ride_duration_min=ride_duration_min
    )

def add_business_area_flag_fast(df, df_station):
    business_dict = df_station.set_index('name')['inside_Business'].to_dict()

    start_inside = df['start_station_name'].map(business_dict)
    end_inside = df['end_station_name'].map(business_dict)
    

    trip_in_business_area = (start_inside.fillna(False) | end_inside.fillna(False))
    df['trip_in_business_area'] = trip_in_business_area
    
    return df

def calculate_costs_fast(df):
    member_mask = df['member_casual'] == 'member'
    casual_mask = df['member_casual'] == 'casual'
    
    df['base_cost'] = 0.0
    df.loc[member_mask, 'base_cost'] = 3.95
    df.loc[casual_mask, 'base_cost'] = 1.00
    
 
    df['extra_minutes'] = 0
    df.loc[member_mask, 'extra_minutes'] = np.maximum(df.loc[member_mask, 'ride_duration_min'] - 45, 0)
    
    df['extra_time_fee'] = 0.0
    df.loc[member_mask & (df['rideable_type'] == 'classic_bike'), 'extra_time_fee'] = df['extra_minutes'] * 0.05
    df.loc[member_mask & (df['rideable_type'] == 'electric_bike'), 'extra_time_fee'] = df['extra_minutes'] * 0.1
    
    df['extra_time_fee_casual'] = 0.0
    df.loc[casual_mask & (df['rideable_type'] == 'classic_bike'), 'extra_time_fee_casual'] = df.loc[casual_mask, 'ride_duration_min'] * 0.05
    df.loc[casual_mask & (df['rideable_type'] == 'electric_bike'), 'extra_time_fee_casual'] = df.loc[casual_mask, 'ride_duration_min'] * 0.15
    
    df['extra_time_fee'] = df['extra_time_fee'] + df['extra_time_fee_casual']
    
    df['long_trip_fee'] = np.where(df['ride_duration_min'] > 1440, 3.0, 0.0)
    
    df['business_area_fee'] = np.where(df['trip_in_business_area'], 0.5, 0.0)
    
    df['total_cost'] = df['base_cost'] + df['extra_time_fee'] + df['long_trip_fee'] + df['business_area_fee']
    
    df['total_cost'] = df['total_cost'].round(2)
    
    df.drop(columns=['extra_minutes', 'extra_time_fee_casual'], inplace=True)
    
    return df

df = (df
      .pipe(add_time_features_fast)
      .pipe(add_business_area_flag_fast, df_station=df_station)
      .pipe(calculate_costs_fast)

     )

print(df_station.columns)

Index(['id', 'name', 'lat', 'lng', 'inside_dc', 'inside_Business'], dtype='object')

print(f"عدد الصفوف التي مدة الرحلة فيها <= 0 دقيقة: {(df['ride_duration_min'] <= 0).sum()}")

df = df[df['ride_duration_min'] > 0]


print(f"عدد الصفوف بعد التنظيف: {df.shape[0]}")

عدد الصفوف التي مدة الرحلة فيها <= 0 دقيقة: 10537
عدد الصفوف بعد التنظيف: 6103742

bins = [0, 15, 25, locations_df['CAPACITY'].max()]
labels = ['Small' , 'Medium', 'Large']

locations_df['CAPACITY_BIN'] = pd.cut(locations_df['CAPACITY'], bins=bins, labels=labels, include_lowest=True)

locations_df['CAPACITY_BIN'].value_counts()

CAPACITY_BIN
Small     417
Medium    327
Large      50
Name: count, dtype: int64

capacity_counts = locations_df['CAPACITY_BIN'].value_counts().reset_index()
capacity_counts.columns = ['Capacity_Bin', 'Count']
"""
fig = px.bar(
    capacity_counts,
    x='Capacity_Bin',
    y='Count',
    labels={'Capacity_Bin': 'Capacity Category', 'Count': 'Number of Stations'},
    title='Number of Stations by Capacity Category',
    color_discrete_sequence=['#636EFA']
)

fig.update_layout(template='plotly_white')
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\146.png'

image = Image.open(image_path)
display(image)

def add_nearest_distance_and_id(df, ref_df, ref_lat_col='LATITUDE', ref_lng_col='LONGITUDE', ref_id_col='ID',
                                start_lat_col='start_lat_y', start_lng_col='start_lng_y',
                                end_lat_col='end_lat_y', end_lng_col='end_lng_y',
                                start_output_col='start_distance_to_ref_m',
                                end_output_col='end_distance_to_ref_m',
                                start_id_output_col='start_nearest_ref_id',
                                end_id_output_col='end_nearest_ref_id'):
   
    ref_coords = np.vstack((ref_df[ref_lng_col].values, ref_df[ref_lat_col].values)).T
    ref_tree = cKDTree(ref_coords)
    
    valid_start_mask = df[start_lat_col].notna() & df[start_lng_col].notna()
    valid_end_mask = df[end_lat_col].notna() & df[end_lng_col].notna()
    
    start_coords = np.vstack((
        df.loc[valid_start_mask, start_lng_col].values,
        df.loc[valid_start_mask, start_lat_col].values
    )).T
    
    end_coords = np.vstack((
        df.loc[valid_end_mask, end_lng_col].values,
        df.loc[valid_end_mask, end_lat_col].values
    )).T
    
    dist_start, idx_start = ref_tree.query(start_coords, k=1)
    dist_end, idx_end = ref_tree.query(end_coords, k=1)
    
    factor_to_meters = 111320
    
    df[start_output_col] = np.nan
    df[end_output_col] = np.nan
    
    df.loc[valid_start_mask, start_output_col] = dist_start * factor_to_meters
    df.loc[valid_end_mask, end_output_col] = dist_end * factor_to_meters
    
    df[start_id_output_col] = np.nan
    df[end_id_output_col] = np.nan
    
    nearest_start_ids = ref_df.iloc[idx_start][ref_id_col].values
    nearest_end_ids = ref_df.iloc[idx_end][ref_id_col].values
    
    df.loc[valid_start_mask, start_id_output_col] = nearest_start_ids
    df.loc[valid_end_mask, end_id_output_col] = nearest_end_ids
    
    return df

df = add_nearest_distance_and_id(df, shuttle_df,
                                 ref_lat_col='LATITUDE', ref_lng_col='LONGITUDE', ref_id_col='ID',
                                 start_output_col='start_distance_to_shuttle_m',
                                 end_output_col='end_distance_to_shuttle_m',
                                 start_id_output_col='start_nearest_shuttle_id',
                                 end_id_output_col='end_nearest_shuttle_id')

df = add_nearest_distance_and_id(df, metro_df,
                                 ref_lat_col='LATITUDE', ref_lng_col='LONGITUDE', ref_id_col='ID',
                                 start_output_col='start_distance_to_metro_m',
                                 end_output_col='end_distance_to_metro_m',
                                 start_id_output_col='start_nearest_metro_id',
                                 end_id_output_col='end_nearest_metro_id')

lat_min = df['end_distance_to_shuttle_m'].min()
lat_max =df['end_distance_to_shuttle_m'].max()
lng_min = df['start_distance_to_shuttle_m'].min()
lng_max = df['start_distance_to_shuttle_m'].max()

print(" نطاق    البعد عن الموقف حافلة:")
print(f": من {lat_min} إلى {lat_max}")
print(f": من {lng_min} إلى {lng_max}")

 نطاق    البعد عن الموقف حافلة:
: من 6.698338510186922 إلى 30531.86919690506
: من 6.698338510186922 إلى 30531.86919690506

lat_min = df['end_distance_to_metro_m'].min()
lat_max =df['end_distance_to_metro_m'].max()
lng_min = df['start_distance_to_metro_m'].min()
lng_max = df['start_distance_to_metro_m'].max()

print(" نطاق البعد عن موقف المترو:")
print(f": من {lat_min} إلى {lat_max}")
print(f": من {lng_min} إلى {lng_max}")

 نطاق البعد عن موقف المترو:
: من 1.266184083096535 إلى 4373.904399448221
: من 1.266184083096535 إلى 4373.904399448221

def prepare_stations(df_station, business_boundary):
    stations = df_station[['name', 'lat', 'lng', 'inside_Business']].copy()
    stations = stations.rename(columns={'name': 'end_station_name'})
    stations['geometry'] = stations.apply(lambda row: Point(row['lng'], row['lat']), axis=1)
    gdf_stations = gpd.GeoDataFrame(stations, geometry='geometry', crs='EPSG:4326')

    gdf_stations = gdf_stations.to_crs(business_boundary.crs)

    gdf_stations['distance_to_business_area'] = gdf_stations.geometry.apply(lambda x: business_boundary.distance(x).min())
    threshold = 500
    gdf_stations['close_to_business_area'] = gdf_stations['distance_to_business_area'] <= threshold

    return gdf_stations[['end_station_name', 'distance_to_business_area', 'close_to_business_area', 'inside_Business']]
def add_distance_flag(df, gdf_stations, df_station):
    start_inside_dict = df_station.set_index('name')['inside_Business'].to_dict()
  
    end_inside_dict = start_inside_dict 
    df = df.merge(gdf_stations[['end_station_name', 'distance_to_business_area', 'close_to_business_area']],
                  on='end_station_name', how='left')

    start_inside = df['start_station_name'].map(start_inside_dict).fillna(False)
    end_inside = df['end_station_name'].map(end_inside_dict).fillna(False)
    mask = start_inside & end_inside
    df.loc[mask, ['distance_to_business_area', 'close_to_business_area']] = np.nan

    return df

gdf_stations_prepared = prepare_stations(df_station, Business_boundary)

df = df.pipe(add_distance_flag, gdf_stations=gdf_stations_prepared, df_station=df_station)

C:\Users\asus\AppData\Local\Temp\ipykernel_12476\257399541.py:12: UserWarning: Geometry is in a geographic CRS. Results from 'distance' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  gdf_stations['distance_to_business_area'] = gdf_stations.geometry.apply(lambda x: business_boundary.distance(x).min())

coords = df_station[['lat', 'lng']].values
tree = cKDTree(coords)

distances, indices = tree.query(coords, k=2)
nearest_distances = distances[:, 1]

nearest_distances_km = nearest_distances * 111 

print("أدنى مسافة بين محطات (كم):", nearest_distances_km.min())
print("متوسط المسافة بين المحطات (كم):", nearest_distances_km.mean())
print("أعلى مسافة بين أقرب محطات (كم):", nearest_distances_km.max())

أدنى مسافة بين محطات (كم): 0.0003214075879907089
متوسط المسافة بين المحطات (كم): 0.43477205898036936
أعلى مسافة بين أقرب محطات (كم): 3.479772189213669

df_station.drop(columns='geohash', inplace=True)
df.drop(columns='start_geohash', inplace=True)
df.drop(columns='end_geohash', inplace=True)

for i, col in enumerate(df.columns):
    print(f"{i}: {col}")

0: ride_id
1: rideable_type
2: started_at
3: ended_at
4: start_station_name
5: start_station_id
6: end_station_name
7: end_station_id
8: start_lat_x
9: start_lng_x
10: end_lat_x
11: end_lng_x
12: member_casual
13: start_lat_y
14: start_lng_y
15: end_lat_y
16: end_lng_y
17: start_year
18: end_year
19: start_month
20: end_month
21: start_day
22: end_day
23: start_day_name
24: end_day_name
25: start_hour_am_pm
26: end_hour_am_pm
27: ride_duration_min
28: trip_in_business_area
29: base_cost
30: extra_time_fee
31: long_trip_fee
32: business_area_fee
33: total_cost
34: start_distance_to_shuttle_m
35: end_distance_to_shuttle_m
36: start_nearest_shuttle_id
37: end_nearest_shuttle_id
38: start_distance_to_metro_m
39: end_distance_to_metro_m
40: start_nearest_metro_id
41: end_nearest_metro_id
42: distance_to_business_area_x
43: close_to_business_area_x
44: distance_to_business_area_y
45: close_to_business_area_y
46: distance_to_business_area
47: close_to_business_area

df_station['geohash'] = df_station.apply(lambda row: geohash.encode(row['lat'], row['lng'], precision=7), axis=1)

df = df.merge(df_station[['name', 'geohash']], how='left', left_on='start_station_name', right_on='name')
df.rename(columns={'geohash': 'start_geohash'}, inplace=True)
df.drop(columns=['name'], inplace=True)
df = df.merge(df_station[['name', 'geohash']], how='left', left_on='end_station_name', right_on='name')
df.rename(columns={'geohash': 'end_geohash'}, inplace=True)
df.drop(columns=['name'], inplace=True)

start_counts = df['start_geohash'].value_counts().reset_index()
start_counts.columns = ['geohash', 'start_count']

end_counts = df['end_geohash'].value_counts().reset_index()
end_counts.columns = ['geohash', 'end_count']
counts = start_counts.merge(end_counts, on='geohash', how='outer').fillna(0)

df['start_date'] = pd.to_datetime(df['started_at']).dt.date
start_counts = df.groupby(['start_geohash', 'start_date']).size().reset_index(name='count')

end_counts = df.groupby(['end_geohash', 'start_date']).size().reset_index(name='count')

start_counts.rename(columns={'start_geohash': 'geohash'}, inplace=True)
end_counts.rename(columns={'end_geohash': 'geohash'}, inplace=True)
all_counts = pd.concat([start_counts, end_counts])

daily_counts = all_counts.groupby(['geohash', 'start_date'])['count'].sum().reset_index()

mean_daily = daily_counts.groupby('geohash')['count'].mean().reset_index()
mean_daily.rename(columns={'count': 'mean_daily_usage'}, inplace=True)

max_usage = mean_daily['mean_daily_usage'].max()

def classify_usage(x):
    pct = (x / max_usage) * 100
    if pct > 70:
        return 'أحمر'
    elif pct > 50:
        return 'أصفر'
    elif pct <= 50:
        return 'رمادي'


mean_daily['category'] = mean_daily['mean_daily_usage'].apply(classify_usage)

mean_daily['category'].value_counts()

category
رمادي    776
أصفر      16
أحمر       8
Name: count, dtype: int64

  def map_conditions(cond):
    cond = cond.lower()
    if 'rain' in cond or 'snow' in cond:
        return 'Rainy'
    elif 'overcast' in cond:
        return 'Cloudy'
    elif 'clear' in cond or 'partially cloudy' in cond:
        return 'Sunny'


weather_df['weather_bin'] = weather_df['conditions'].apply(map_conditions)

weather_df['weather_bin'].value_counts()

weather_bin
Sunny     221
Rainy     139
Cloudy      6
Name: count, dtype: int64

weather_counts = weather_df['weather_bin'].value_counts().reset_index()
weather_counts.columns = ['Weather', 'Count']
"""
fig = px.bar(
    weather_counts,
    x='Weather',
    y='Count',
    title='Weather Conditions Distribution',
    color='Weather',
    color_discrete_sequence=px.colors.qualitative.Set2
)

fig.update_layout(template='plotly_white')
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\170.png'

image = Image.open(image_path)
display(image)

df['end_date'] = df['ended_at'].dt.date
daily_revenue = (
    df.groupby('end_date')['total_cost']
    .sum()
    .reset_index()
    .rename(columns={'end_date': 'date', 'total_cost': 'daily_revenue'})
)
weather_df['date'] = pd.to_datetime(weather_df['datetime']).dt.date
daily_revenue_weather = pd.merge(
    daily_revenue,
    weather_df[['date', 'weather_bin']], 
    on='date',
    how='left'
)

print(daily_revenue_weather.head())

         date  daily_revenue weather_bin
0  2024-01-01       18181.08       Rainy
1  2024-01-02       32445.09       Sunny
2  2024-01-03       34860.97       Sunny
3  2024-01-04       33082.30       Sunny
4  2024-01-05       31989.71       Sunny

"""
fig = px.line(
    daily_revenue_weather,
    x='date',
    y='daily_revenue',
    color='weather_bin',  # يلون حسب حالة الطقس
    title='Daily Total Revenue with Weather Condition',
    labels={
        'date': 'Date',
        'daily_revenue': 'Total Revenue',
        'weather_bin': 'Weather Condition'
    }
)

# تحسين المظهر
fig.update_layout(
    xaxis_title='Date',
    yaxis_title='Total Revenue',
    hovermode='x unified'
)

fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\181.png'
image = Image.open(image_path)
display(image)

df_station.to_csv(r'C:\Users\ASUS\OneDrive\Desktop\stationsc.csv', index=False)
df.to_parquet(r'C:\Users\ASUS\OneDrive\Desktop\daily-rentc.parquet', index=False)
metro_df.to_csv(r'C:\Users\ASUS\OneDrive\Desktop\Metro_Bus_Stopsc.csv', index=False)

shuttle_df.to_csv(r'C:\Users\ASUS\OneDrive\Desktop\Shuttle_Bus_Stopsc.csv', index=False)
weather_df.to_csv(r'C:\Users\ASUS\OneDrive\Desktop\Washington,DC,USA 2024-01-01 to 2024-12-31c.csv', index=False)
locations_df.to_csv(r'C:\Users\ASUS\OneDrive\Desktop\Capital_Bikeshare_Locationsc.csv', index=False)

df_station = pd.read_csv(r'C:\Users\ASUS\OneDrive\Desktop\stationsc.csv')

df = pd.read_parquet(r'C:\Users\ASUS\OneDrive\Desktop\daily-rentc.parquet')


metro_df= pd.read_csv(r'C:\Users\ASUS\OneDrive\Desktop\Metro_Bus_Stopsc.csv')
shuttle_df = pd.read_csv(r'C:\Users\ASUS\OneDrive\Desktop\Shuttle_Bus_Stopsc.csv')

weather_df = pd.read_csv(r'C:\Users\ASUS\OneDrive\Desktop\Washington,DC,USA 2024-01-01 to 2024-12-31c.csv') 

locations_df = pd.read_csv(r'C:\Users\ASUS\OneDrive\Desktop\Capital_Bikeshare_Locationsc.csv')

df['start_station_name'] = df['start_station_name'].replace('nan', np.nan)
df['end_station_name'] = df['end_station_name'].replace('nan', np.nan)

cbd_gdf = gpd.read_file(r'C:\Users\ASUS\Downloads\data-20250530T151946Z-1-001\data\DDOT_Central_Business_District.geojson')
parking_zones_gdf = gpd.read_file(r'C:\Users\ASUS\Downloads\data-20250530T151946Z-1-001\data\Residential_and_Visitor_Parking_Zones.geojson')
dc_boundary = gpd.read_file(r'C:\Users\ASUS\Downloads\Washington_DC_Boundary_Stone_Area.geojson')
Business_boundary =gpd.read_file(r'C:\Users\ASUS\Downloads\data-20250530T151946Z-1-001\data\DDOT_Central_Business_District.geojson')

top5_start_stations = (
    df['start_station_name']
    .value_counts()
    .head(5)
    .reset_index()
    .rename(columns={'index': 'start_station_name', 'count': 'trip_count'})
)

""""
fig = px.bar(
    top5_start_stations,
    x='start_station_name',
    y='trip_count',
    color='start_station_name',
    text='trip_count',
    title='Top 5 محطات الانطلاق الأكثر استخداماً',
    labels={'start_station_name': 'اسم محطة الانطلاق', 'trip_count': 'عدد الرحلات'}
)


fig.update_layout(
    xaxis_title='محطة الانطلاق',
    yaxis_title='عدد الرحلات',
    showlegend=False
)

fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\a1.png'
image = Image.open(image_path)
display(image)

grouped_df = (
    df.groupby(['rideable_type', 'member_casual'])
    .size()
    .reset_index(name='trip_count')
)

"""

fig = px.bar(
    grouped_df,
    x='rideable_type',
    y='trip_count',
    color='member_casual',
    barmode='group',  # عرض كل مجموعة جنباً إلى جنب
    text='trip_count',
    title='توزيع عدد الرحلات حسب نوع الدراجة ونوع الاشتراك',
    labels={'rideable_type': 'نوع الدراجة', 'trip_count': 'عدد الرحلات', 'member_casual': 'نوع الاشتراك'}
)

fig.update_layout(
    xaxis_title='نوع الدراجة',
    yaxis_title='عدد الرحلات',
    legend_title='نوع الاشتراك'
)

fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\a2.png'
image = Image.open(image_path)
display(image)

top5_stations = top5_start_stations['start_station_name'].tolist()
df_top5 = df[df['start_station_name'].isin(top5_stations)]

sunburst_df = (
    df_top5.groupby(['start_station_name', 'rideable_type', 'member_casual'])
    .size()
    .reset_index(name='trip_count')
)

"""
fig = px.sunburst(
    sunburst_df,
    path=['start_station_name', 'rideable_type', 'member_casual'],
    values='trip_count',
    color='rideable_type',  # تلوين حسب نوع الدراجة
    title='توزيع الرحلات حسب أفضل 5 محطات، نوع الدراجة، ونوع الاشتراك',
)

fig.update_layout(margin=dict(t=40, l=0, r=0, b=0))
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\a3.png'
image = Image.open(image_path)
display(image)

"""
fig = px.histogram(
    locations_df,
    x='CAPACITY',
    nbins=20,
    title='Distribution of Station Capacity',
    labels={'CAPACITY': 'Station Capacity', 'count': 'Number of Stations'}
)

fig.update_layout(
    xaxis_title='Station Capacity',
    yaxis_title='Number of Stations'
)

fig.show()
"""


image_path = r'C:\Users\ASUS\OneDrive\Desktop\a4.png'

image = Image.open(image_path)
display(image)

capacity_bin_map = locations_df.set_index('NAME')['CAPACITY_BIN'].to_dict()
df['start_capacity_bin'] = df['start_station_name'].map(capacity_bin_map)
df['end_capacity_bin'] = df['end_station_name'].map(capacity_bin_map)

start_bin_counts = df['start_capacity_bin'].value_counts().reset_index()
start_bin_counts.columns = ['capacity_bin', 'trip_count']
"""
fig = px.bar(
    start_bin_counts,
    x='capacity_bin',
    y='trip_count',
    title='Trip Distribution by Start Station Capacity Bin',
    labels={'capacity_bin': 'Start Station Capacity Bin', 'trip_count': 'Number of Trips'}
)

fig.show()
"""

image_path = r'C:\Users\ASUS\OneDrive\Desktop\a4_2.png'

image = Image.open(image_path)
display(image)

end_bin_counts = df['end_capacity_bin'].value_counts().reset_index()
end_bin_counts.columns = ['capacity_bin', 'trip_count']
"""
fig = px.bar(
    end_bin_counts,
    x='capacity_bin',
    y='trip_count',
    title='Trip Distribution by End Station Capacity Bin',
    labels={'capacity_bin': 'End Station Capacity Bin', 'trip_count': 'Number of Trips'}
)

fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\a4_3.png'
image = Image.open(image_path)
display(image)

bins = [-float('inf'), 1, 3, 5, 7, 10, 15, 30, 60, 120, 240, 1440, float('inf')]
labels = [
    'Under 1 minute',
    '1-3 minutes',
    '3-5 minutes',
    '5-7 minutes',
    '7-10 minutes',
    '10-15 minutes',
    '15-30 minutes',
    '30-60 minutes',
    '1-2 hours',
    '2-4 hours',
    '4 hours - 1 day',
    'Over 1 day'
]


df['duration_category'] = pd.cut(df['ride_duration_min'], bins=bins, labels=labels, right=False)

print(df['duration_category'].value_counts())

duration_category
15-30 minutes      1226972
10-15 minutes      1180431
7-10 minutes       1047759
5-7 minutes         803570
3-5 minutes         750383
30-60 minutes       430980
1-3 minutes         405324
1-2 hours           115624
Under 1 minute      101274
2-4 hours            28504
4 hours - 1 day       7893
Over 1 day            5028
Name: count, dtype: int64

duration_counts = df['duration_category'].value_counts().sort_index()
duration_df = duration_counts.reset_index()
duration_df.columns = ['مدة الرحلة', 'عدد الرحلات']
"""
fig = px.bar(
    duration_df,
    x='مدة الرحلة',
    y='عدد الرحلات',
    text='عدد الرحلات',
    title='توزع عدد الرحلات حسب فئات مدة الرحلة',
    labels={'مدة الرحلة': 'مدة الرحلة', 'عدد الرحلات': 'عدد الرحلات'},
    color='عدد الرحلات',
    color_continuous_scale='Blues'
)

fig.update_traces(texttemplate='%{text:,}', textposition='outside')
fig.update_layout(
    xaxis_title='فئة مدة الرحلة',
    yaxis_title='عدد الرحلات',
    uniformtext_minsize=8,
    uniformtext_mode='hide',
    margin=dict(t=40, b=80),
    coloraxis_showscale=False
)

fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\b1.png'
image = Image.open(image_path)
display(image)

"""
fig = px.box(
    df,
    x='rideable_type',
    y='ride_duration_min',
    title='توزع مدة الرحلة حسب نوع الدراجة',
    labels={
        'rideable_type': 'نوع الدراجة',
        'ride_duration_min': 'مدة الرحلة (دقائق)'
    },
    points='outliers' 
)

fig.show()
"""
"""
sample_df = df.sample(n=100000, random_state=42)
fig = px.box(
sample_df,
   x='rideable_type',
    y='ride_duration_min',
    title='توزع مدة الرحلة حسب نوع الدراجة',
    labels={
        'rideable_type': 'نوع الدراجة',
        'ride_duration_min': 'مدة الرحلة (دقائق)'
    },
    points='outliers' 
)

fig.show()
"""

image_path = r'C:\Users\ASUS\OneDrive\Desktop\b2.png'
image = Image.open(image_path)
display(image)
image_path = r'C:\Users\ASUS\OneDrive\Desktop\b3ss.png'
image = Image.open(image_path)
display(image)

"""
fig = px.box(
    df,
    x='rideable_type',
    y='ride_duration_min',
    points='outliers',  # عرض النقاط الشاذة
    title='Box Plot of Ride Duration by Rideable Type (Log Scale)',
    labels={
        'rideable_type': 'Rideable Type',
        'ride_duration_min': 'Ride Duration (minutes)'
    }
)

# نحط المحور Y لوغاريتمي
fig.update_yaxes(type='log')

fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\b2log.png'
image = Image.open(image_path)
display(image)

sample_df = df.sample(n=100000, random_state=42)
"""
fig = px.box(
 df,
    x='member_casual',
    y='ride_duration_min',
    title='توزع مدة الرحلة حسب نوع اشتراك',
    labels={
        'member_casual': 'member Type',
        'ride_duration_min': 'مدة الرحلة (دقائق)'
    },
    points='outliers' 
)
fig.show()
"""
"""
fig = px.box(
sample_df,
    x='member_casual',
    y='ride_duration_min',
    title='توزع مدة الرحلة حسب نوع اشتراك',
    labels={
        'member_casual': 'member Type',
        'ride_duration_min': 'مدة الرحلة (دقائق)'
    },
    points='outliers' 
)
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\b31.png'

image = Image.open(image_path)
display(image)
image_path = r'C:\Users\ASUS\OneDrive\Desktop\b31ss.png'

image = Image.open(image_path)
display(image)

"""
fig = px.box(
    df,
    x='member_casual',
    y='ride_duration_min',
    points='outliers',  # عرض النقاط الشاذة
    title='Box Plot of Ride Duration by member Type (Log Scale)',
    labels={
        'member_casual': 'member Type',
        'ride_duration_min': 'Ride Duration (minutes)'
    }
)

# نحط المحور Y لوغاريتمي
fig.update_yaxes(type='log')

fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\b32.png'

image = Image.open(image_path)
display(image)

# df_long_trips = df[df['ride_duration_min'] > 1440].copy()
# start_counts = (
#     df_long_trips.groupby('start_station_name')
#     .size()
#     .reset_index(name='start_trip_count')
# )
# end_counts = (
#     df_long_trips.groupby('end_station_name')
#     .size()
#     .reset_index(name='end_trip_count')
# )

# station_counts = pd.merge(
#     start_counts, 
#     end_counts, 
#     left_on='start_station_name', 
#     right_on='end_station_name', 
#     how='outer'
# )

# station_counts['station_name'] = station_counts['start_station_name'].combine_first(station_counts['end_station_name'])
# station_counts['start_trip_count'] = station_counts['start_trip_count'].fillna(0)
# station_counts['end_trip_count'] = station_counts['end_trip_count'].fillna(0)

# station_counts['total_long_trips'] = station_counts['start_trip_count'] + station_counts['end_trip_count']

# stations_geo = df[['start_station_name', 'start_lat_y', 'start_lng_y']].drop_duplicates()
# stations_geo = stations_geo.rename(columns={
#     'start_station_name': 'station_name',
#     'start_lat_y': 'lat',
#     'start_lng_y': 'lng'
# })

# stations_plot = pd.merge(stations_geo, station_counts[['station_name', 'total_long_trips']], on='station_name', how='left')
# stations_plot['total_long_trips'] = stations_plot['total_long_trips'].fillna(0)

# dc_boundary = gpd.read_file(r'C:\Users\ASUS\Downloads\Washington_DC_Boundary_Stone_Area.geojson')
# if dc_boundary.crs != "EPSG:4326":
#     dc_boundary = dc_boundary.to_crs(epsg=4326)

# dc_geojson = dc_boundary.__geo_interface__

# fig = px.choropleth_mapbox(
#     geojson=dc_geojson,
#     locations=dc_boundary.index,
#     color_discrete_sequence=["lightgray"],
#     center={"lat": 38.9072, "lon": -77.0369},
#     mapbox_style="carto-positron",
#     zoom=10,
#     opacity=0.4
# )

# fig.add_scattermapbox(
#     lat=stations_plot['lat'],
#     lon=stations_plot['lng'],
#     mode='markers',
#     marker=dict(
#         size=stations_plot['total_long_trips'] / stations_plot['total_long_trips'].max() * 40 + 5,  
#         color='red',
#         opacity=0.7
#     ),
#     text=stations_plot['station_name'] + '<br>Total long trips: ' + stations_plot['total_long_trips'].astype(int).astype(str),
#     name='Stations with long trips'
# )

# fig.update_layout(
#     title="محطات الدراجات التي استقبلت أو أطلقت رحلات تجاوزت يوم واحد",
#     margin={"r":0,"t":30,"l":0,"b":0}
# )

# fig.show()



image_path = r'C:\Users\ASUS\OneDrive\Desktop\b4.png'
image = Image.open(image_path)
display(image)

bins = [1,2 ,3,3.95,4, 5, 7, 10, 15, 30, 60, 120]
labels = [
    '1–2 $',
    '2–3 $',
    '3–3.95 $',
     '3.95–4 $',
    '4–5 $',
    '5–7 $',
    '7–10 $',
    '10–15 $',
    '15–30 $',
    '30–60 $',
    '60–120 $',

]

df['total_category'] = pd.cut(df['total_cost'], bins=bins, labels=labels, right=False)

print(df['total_category'].value_counts())

total_category
3.95–4 $    3011977
4–5 $       1147289
1–2 $        717707
2–3 $        677682
3–3.95 $     304753
5–7 $        129323
7–10 $        60219
10–15 $       29250
15–30 $       15738
60–120 $       6010
30–60 $        3794
Name: count, dtype: int64

df_filtered = df[df['total_category'].notna()]
"""
fig = px.histogram(
    df_filtered,
    x='total_category',
    color_discrete_sequence=['lightblue'],
    nbins=len(df_filtered['total_category'].unique()),
    opacity=0.6,
    histnorm=None
)

box_fig = px.box(
    df_filtered,
    x='total_category',
    y='total_cost',
    points='outliers',
    color_discrete_sequence=['darkblue']
)

for trace in box_fig.data:
    fig.add_trace(trace)

fig.update_layout(
    title='توزيع تكلفة الرحلات حسب الفئات',
    xaxis_title='فئة التكلفة',
    yaxis_title='عدد الرحلات / تكلفة ($)',
    barmode='overlay',
    template='plotly_white',
    yaxis_type="log",        # مقياس لوغاريتمي
    xaxis_tickangle=-45,     # تدوير التسميات على المحور X
    bargap=0.2,              # مسافة بين الأعمدة
    width=1200               # عرض الرسم
)

fig.show()

"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\c1.png'
image = Image.open(image_path)
display(image)

"""
df_filtered = df[df['total_cost'].notna() & df['ride_duration_min'].notna()]

fig = px.scatter(
    df_filtered,
    x='ride_duration_min',
    y='total_cost',
    trendline='ols', 
    opacity=0.5,
    color_discrete_sequence=['darkblue']
)

fig.update_layout(
    title='مخطط تكلفة الرحلة مقابل المدة الزمنية مع خط الاتجاه',
    xaxis_title='المدة الزمنية (بالدقائق أو بالساعات حسب البيانات)',
    yaxis_title='تكلفة الرحلة ($)',
    template='plotly_white',
    width=1000,
    height=600
)

fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\c2.png'
image = Image.open(image_path)
display(image)

"""
df['date'] = pd.to_datetime(df['started_at']).dt.date
weather_df['date'] = pd.to_datetime(weather_df['date']).dt.date
merged_df = pd.merge(df, weather_df[['date', 'temp']], on='date', how='left')

filtered_df = merged_df.dropna(subset=['total_cost', 'temp', 'rideable_type'])
"""

"""
fig = px.scatter(
    filtered_df,
    x='temp',
    y='total_cost',
    color='rideable_type', 
    opacity=0.6,
    title='تكلفة الرحلة مقابل درجة الحرارة بحسب فئة الدراجة',
    labels={'temp': 'درجة الحرارة (°C)', 'total_cost': 'تكلفة الرحلة ($)', 'rideable_type': 'فئة الدراجة'},
    template='plotly_white'
)

fig.update_traces(marker=dict(size=6))
fig.update_layout(width=1000, height=600)
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\c3.png'

image = Image.open(image_path)
display(image)

df['start_date'] = pd.to_datetime(df['started_at'].dt.date)
df['start_week']=df['started_at'].dt.to_period('W').apply(lambda r : r.start_time)

daily_revenue = df.groupby('start_date')['total_cost'].sum().reset_index()
daily_revenue['type'] = 'Daily'
weekly_revenue = df.groupby('start_week')['total_cost'].sum().reset_index()
weekly_revenue = weekly_revenue.rename(columns={'start_week': 'start_date'}) 
weekly_revenue['type'] = 'Weekly'

combined_revenue = pd.concat([daily_revenue, weekly_revenue])

fig = px.line(combined_revenue, x='start_date', y='total_cost', color='type',
              title='الإيرادات اليومية والأسبوعية',
              labels={'start_date': 'التاريخ', 'total_cost': 'الإيرادات', 'type': 'نوع الإيراد'},
              markers=True)

fig.update_layout(template='plotly_dark')  
# fig.show()
image_path = r"C:\Users\ASUS\OneDrive\Desktop\images\1.png"

image = Image.open(image_path)
display(image)

df['start_month'] = df['started_at'].dt.to_period('M').astype(str)
monthly_avg = df.groupby('start_month')['total_cost'].mean().reset_index()

# fig = px.line(monthly_avg, x='start_month', y='total_cost', markers=True,
#               title='متوسط الإيرادات الشهرية',
#               labels={'start_month': 'الشهر', 'total_cost': 'متوسط الإيرادات (دولار)'})

# fig.update_layout(template='plotly_white', xaxis_tickangle=-45)
# fig.show()
image_path = r"C:\Users\ASUS\OneDrive\Desktop\images\2.png"
image = Image.open(image_path)
display(image)

df['start_geometry'] = [Point(xy) for xy in zip(df['start_lng_x'], df['start_lat_x'])]
df['end_geometry'] = [Point(xy) for xy in zip(df['end_lng_x'], df['end_lat_x'])]

start_gdf = gpd.GeoDataFrame(df, geometry='start_geometry', crs='EPSG:4326')
end_gdf = gpd.GeoDataFrame(df, geometry='end_geometry', crs='EPSG:4326')
parking_zones_gdf = parking_zones_gdf.to_crs(epsg=4326)

parking_zones_gdf = parking_zones_gdf.to_crs(epsg=4326)

start_join = gpd.sjoin(
    start_gdf[['ride_id', 'start_geometry']],
    parking_zones_gdf[['NAME', 'geometry']],
    how='left',
    predicate='within'
).rename(columns={'NAME': 'start_zone'})

end_join = gpd.sjoin(
    end_gdf[['ride_id', 'end_geometry']],
    parking_zones_gdf[['NAME', 'geometry']],
    how='left',
    predicate='within'
).rename(columns={'NAME': 'end_zone'})

zone_counts = pd.concat([
    start_join['start_zone'],
    end_join['end_zone']
]).value_counts().reset_index()

zone_counts.columns = ['ZONE_NAME', 'trip_count']

parking_zones_gdf = parking_zones_gdf.merge(zone_counts, left_on='NAME', right_on='ZONE_NAME', how='left')
parking_zones_gdf.drop(columns=['ZONE_NAME'], inplace=True)
parking_zones_gdf['trip_count'] = parking_zones_gdf['trip_count'].fillna(0)

outside_trips_start = start_join['start_zone'].isna().sum()
outside_trips_end = end_join['end_zone'].isna().sum()

total_outside = outside_trips_start + outside_trips_end

print(f"عدد الرحلات خارج المناطق السكنية: {total_outside}")

عدد الرحلات خارج المناطق السكنية: 1649311

"""
fig = px.choropleth_mapbox(
    parking_zones_gdf,
    geojson=parking_zones_gdf.geometry.__geo_interface__,
    locations=parking_zones_gdf.index,
    color='trip_count',
    mapbox_style="carto-positron",
    center={"lat": parking_zones_gdf.geometry.centroid.y.mean(), "lon": parking_zones_gdf.geometry.centroid.x.mean()},
    zoom=10,
    color_continuous_scale="YlOrRd",
    title="Geographic Heatmap of Trips per Residential Zone"
)
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\d1.png'
image = Image.open(image_path)
display(image)

"""

usage_sum = mean_daily.groupby('category')['mean_daily_usage'].sum().reset_index()
usage_sum['category'] = pd.Categorical(usage_sum['category'], categories=['رمادي', 'أصفر', 'أحمر'], ordered=True)

fig = px.bar(
    usage_sum,
    x='category',
    y='mean_daily_usage',
    color='category',
    color_discrete_map={'رمادي':'gray', 'أصفر':'yellow', 'أحمر':'red'},
    title='مجموع متوسط الرحلات اليومية لكل فئة من القطاعات الجغرافية',
    labels={'category':'الفئة', 'mean_daily_usage':'مجموع متوسط الرحلات اليومية'}
)
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\d2.png'
image = Image.open(image_path)
display(image)

distance_columns = [
    'distance_to_business_area',
    'start_distance_to_shuttle_m',
    'start_distance_to_metro_m'
]

df_melted = df[distance_columns].melt(
    var_name='distance_type',
    value_name='distance_value'
).dropna()

# custom_colors = {
#     'distance_to_business_area': 'black',
#     'start_distance_to_shuttle_m': 'green',
#     'start_distance_to_metro_m': 'red'
# }

# fig = px.histogram(
#     df_melted,
#     x='distance_value',
#     color='distance_type',
#     nbins=50,
#     barmode='overlay',
#     title='Histogram لتوزيع المسافات المختلفة',
#     color_discrete_map=custom_colors
# )

# fig.update_layout(
#     xaxis_title='المسافة (متر)',
#     yaxis_title='عدد التكرارات',
#     legend_title_text='نوع المسافة',
#     bargap=0.2,
#     template='plotly_white'  
# )

# fig.show()

image_path = r"C:\Users\ASUS\OneDrive\Desktop\images\3.4.png"
image = Image.open(image_path)
display(image)

df.drop(columns=['start_inside_business', 'end_inside_business'], errors='ignore', inplace=True)
df_station_unique = df_station.drop_duplicates(subset='name')

station_business = df_station_unique.set_index('name')['inside_Business']
df['start_inside_business'] = df['start_station_name'].map(station_business)
df['end_inside_business'] = df['end_station_name'].map(station_business)
df['business_trip_type'] = np.select(
    [
        df['start_inside_business'].isna() | df['end_inside_business'].isna(),
        df['start_inside_business'] & df['end_inside_business']
    ],
    [
        'unkhnown',
        'in'
    ],
    default='out'
)

"""

vc = df['business_trip_type'].value_counts().reset_index()
vc.columns = ['business_trip_type', 'count']

fig = px.bar(
    vc,
    x='business_trip_type',
    y='count',
    labels={'business_trip_type': 'نوع الرحلة', 'count': 'عدد الرحلات'},
    title='توزيع الرحلات حسب المنطقة التجارية',
    template='plotly_white',
    color='business_trip_type',
    color_discrete_sequence=['lightgreen', 'salmon', 'gray']
)

fig.update_layout(showlegend=False)
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\d4.png'
image = Image.open(image_path)
display(image)

df_inside = df[df['business_trip_type'] == 'in']
counts = df_inside.groupby(['rideable_type', 'member_casual']).size().reset_index(name='count')
"""
fig = px.bar(
    counts,
    x='rideable_type',
    y='count',
    color='member_casual',
    barmode='group',
    title='توزيع الرحلات داخل المنطقة التجارية حسب نوع الدراجة ونوع الاشتراك',
    labels={
        'rideable_type': 'نوع الدراجة',
        'member_casual': 'نوع الاشتراك',
        'count': 'عدد الرحلات'
    },
    template='plotly_white',
    color_discrete_sequence=px.colors.qualitative.Set2
)

fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\d5.png'
image = Image.open(image_path)
display(image)

bins = [0, 0.005, 0.015, 0.03, df['distance_to_business_area_x'].max() + 0.01]
labels = ['Very Close', 'Close', 'Medium', 'Far']
df['distance_bin'] = pd.cut(df['distance_to_business_area_x'], bins=bins, labels=labels, include_lowest=True)
contingency_table = pd.crosstab(df['distance_bin'], df['member_casual'])
print(contingency_table)
chi2, p, dof, expected = chi2_contingency(contingency_table)
print("Chi-square value:", chi2)
print("P-value:", p)

if p < 0.05:
    print("يوجد ارتباط احصائي بين المسافة ونوع الاشتراك")
else:
    print("لا يوجد ارتباط احصائي بين المسافة ونوع الاشتراك")

member_casual  casual   member
distance_bin                  
Very Close     576480  1025146
Close          463104   916950
Medium         276155   565130
Far            304386   443677
Chi-square value: 13877.358622143238
P-value: 0.0
يوجد ارتباط احصائي بين المسافة ونوع الاشتراك

weather_melted = weather_df.melt(
    id_vars='datetime',
    value_vars=['temp', 'humidity', 'windspeed'],
    var_name='Variable',
    value_name='Value'
)

"""
fig = px.line(
    weather_melted,
    x='datetime',
    y='Value',
    color='Variable',
    title='Daily Averages: Temperature, Humidity, and Wind Speed'
)

fig.update_layout(template='plotly_white')
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\e1.png'

image = Image.open(image_path)
display(image)

df['ended_at'] = pd.to_datetime(df['ended_at'], errors='coerce')
df['date'] = df['ended_at'].dt.date
daily_revenue_df = df.groupby('date')['total_cost'].sum().reset_index()
daily_revenue_df.rename(columns={'total_cost': 'total_revenue'}, inplace=True)

weather_df['date'] = weather_df['datetime'].dt.date
daily_revenue_df = daily_revenue_df.merge(
    weather_df[['date', 'weather_bin']],
    on='date',
    how='left'
)

"""
fig = px.box(
    daily_revenue_df,
    x='weather_bin',
    y='total_revenue',
    color='weather_bin',
    title='Daily Revenue by Weather Condition',
    labels={'weather_bin': 'Weather Condition', 'total_revenue': 'Total Revenue'},
    template='plotly_white'
)
fig.show()
"""
image_path = r'C:\Users\ASUS\OneDrive\Desktop\e2.png'

image = Image.open(image_path)
display(image)

weather_df['date'] = weather_df['datetime'].dt.date
merged_df = daily_revenue_df.merge(
    weather_df[['date', 'temp', 'humidity']],
    on='date',
    how='left'
)
correlation_temp = merged_df['total_revenue'].corr(merged_df['temp'])
correlation_humidity = merged_df['total_revenue'].corr(merged_df['humidity'])

print("معامل الارتباط بين الإيرادات ودرجة الحرارة:", correlation_temp)
print("معامل الارتباط بين الإيرادات والرطوبة:", correlation_humidity)

معامل الارتباط بين الإيرادات ودرجة الحرارة: 0.6889976439843918
معامل الارتباط بين الإيرادات والرطوبة: -0.15588133323001235

df['date'] = pd.to_datetime(df['ended_at']).dt.date
weather_df['date'] = pd.to_datetime(weather_df['datetime']).dt.date
merged_df = df.merge(weather_df[['date', 'weather_bin']], on='date', how='left')

contingency_table = pd.crosstab(merged_df['weather_bin'], merged_df['rideable_type'])
print(contingency_table)

chi2, p, dof, expected = chi2_contingency(contingency_table)

print("Chi-square value:", chi2)
print("P-value:", p)

if p < 0.05:
    print("يوجد ارتباط إحصائي بين حالة الطقس ونوع الدراجة")
else:
    print("لا يوجد ارتباط إحصائي بين حالة الطقس ونوع الدراجة")

rideable_type  classic_bike   electric_bike
weather_bin                                
Cloudy                 19977          31307
Rainy                 909763        1142249
Sunny                1777651        2222795
Chi-square value: 617.2363368229555
P-value: 9.307487117997466e-135
يوجد ارتباط إحصائي بين حالة الطقس ونوع الدراجة

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6103742 entries, 0 to 6103741
Data columns (total 64 columns):
 #   Column                       Dtype         
---  ------                       -----         
 0   ride_id                      string        
 1   rideable_type                string        
 2   started_at                   datetime64[ns]
 3   ended_at                     datetime64[ns]
 4   start_station_name           object        
 5   start_station_id             Int64         
 6   end_station_name             object        
 7   end_station_id               Int64         
 8   start_lat_x                  float64       
 9   start_lng_x                  float64       
 10  end_lat_x                    float64       
 11  end_lng_x                    float64       
 12  member_casual                string        
 13  start_lat_y                  float64       
 14  start_lng_y                  float64       
 15  end_lat_y                    float64       
 16  end_lng_y                    float64       
 17  start_year                   int32         
 18  end_year                     int32         
 19  start_month                  int32         
 20  end_month                    int32         
 21  start_day                    int32         
 22  end_day                      int32         
 23  start_day_name               object        
 24  end_day_name                 object        
 25  start_hour_am_pm             object        
 26  end_hour_am_pm               object        
 27  ride_duration_min            float64       
 28  trip_in_business_area        bool          
 29  base_cost                    float64       
 30  extra_time_fee               float64       
 31  long_trip_fee                float64       
 32  business_area_fee            float64       
 33  total_cost                   float64       
 34  start_distance_to_shuttle_m  float64       
 35  end_distance_to_shuttle_m    float64       
 36  start_nearest_shuttle_id     float64       
 37  end_nearest_shuttle_id       float64       
 38  start_distance_to_metro_m    float64       
 39  end_distance_to_metro_m      float64       
 40  start_nearest_metro_id       float64       
 41  end_nearest_metro_id         float64       
 42  distance_to_business_area_x  float64       
 43  close_to_business_area_x     object        
 44  distance_to_business_area_y  float64       
 45  close_to_business_area_y     object        
 46  distance_to_business_area    float64       
 47  close_to_business_area       float64       
 48  start_geohash                object        
 49  end_geohash                  object        
 50  start_date                   datetime64[ns]
 51  end_date                     object        
 52  start_capacity_bin           object        
 53  end_capacity_bin             object        
 54  duration_category            category      
 55  total_category               category      
 56  start_week                   datetime64[ns]
 57  start_geometry               object        
 58  end_geometry                 object        
 59  start_inside_business        object        
 60  end_inside_business          object        
 61  business_trip_type           object        
 62  distance_bin                 category      
 63  date                         object        
dtypes: Int64(2), bool(1), category(3), datetime64[ns](4), float64(26), int32(6), object(19), string(3)
memory usage: 2.6+ GB

weather_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 366 entries, 0 to 365
Data columns (total 17 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   name           366 non-null    object        
 1   datetime       366 non-null    datetime64[ns]
 2   tempmax        366 non-null    float64       
 3   tempmin        366 non-null    float64       
 4   temp           366 non-null    float64       
 5   humidity       366 non-null    float64       
 6   windspeed      366 non-null    float64       
 7   windspeedmax   366 non-null    float64       
 8   windspeedmean  366 non-null    float64       
 9   windspeedmin   366 non-null    float64       
 10  cloudcover     366 non-null    float64       
 11  sunrise        366 non-null    datetime64[ns]
 12  sunset         366 non-null    datetime64[ns]
 13  conditions     366 non-null    object        
 14  temp_range     366 non-null    category      
 15  weather_bin    366 non-null    object        
 16  date           366 non-null    object        
dtypes: category(1), datetime64[ns](3), float64(9), object(4)
memory usage: 46.6+ KB

daily_revenue = daily_revenue.sort_values('start_date')

last_10_days = daily_revenue['total_cost'].tail(10).reset_index(drop=True)

daily_diffs = last_10_days.diff().dropna()
avg_daily_change = daily_diffs.mean()

last_value = last_10_days.iloc[-1]

baseline_values = [last_value + avg_daily_change * (i + 1) for i in range(10)]

last_date = daily_revenue['start_date'].max()
future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=10)

baseline_forecast_linear = pd.DataFrame({
    'start_date': future_dates,
    'baseline_total_cost': baseline_values
})
baseline_forecast_linear

import plotly.express as px
import pandas as pd

daily_revenue_renamed = daily_revenue.rename(columns={'total_cost': 'baseline_total_cost'})
daily_revenue_renamed['type'] = 'actual'
baseline_forecast_linear['type'] = 'linear_baseline'
full_df = pd.concat([
    daily_revenue_renamed[['start_date', 'baseline_total_cost', 'type']],
    baseline_forecast_linear
    
], ignore_index=True)

# fig = px.line(
#     full_df,
#     x='start_date',
#     y='baseline_total_cost',
#     color='type',
#     title='Actual vs. Linear Baseline',
#     markers=True
# )

# fig.update_layout(
#     xaxis_title='Date',
#     yaxis_title='Total Cost',
#     legend_title='Data Type',
#     hovermode='x unified'
# )

# fig.show()
image_path = r"C:\Users\ASUS\OneDrive\Desktop\images\10.png"
image = Image.open(image_path)
display(image)

df_prophet = daily_revenue[['start_date', 'total_cost']].rename(
    columns={'start_date': 'ds', 'total_cost': 'y'}
)

from prophet import Prophet

model = Prophet()
model.fit(df_prophet)
#seasonality_mode='multiplicative'

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<prophet.forecaster.Prophet at 0x24530362310>

future = model.make_future_dataframe(periods=10)

forecast = model.predict(future)

from prophet.plot import plot_plotly
import plotly.graph_objects as go

fig = plot_plotly(model, forecast)
# fig.update_layout(title='Prophet Forecast for Total Cost')
# fig.show()
image_path = r"C:\Users\ASUS\OneDrive\Desktop\images\Screenshot 2025-06-20 193703.png"

image = Image.open(image_path)
display(image)

results = []
train = df_prophet.iloc[:-10]  
test = df_prophet.iloc[-10:]
seasonality_modes = ['additive', 'multiplicative']

changepoint_values=[ 0.001,0.1, 0.3, 0.5,0.8,0.9]
n_changepoints=[50,60,70,80,90]
fouriers=[5,10,15,20]
for cp in tqdm(changepoint_values):
    for mode in seasonality_modes:
        for n in n_changepoints:
            for f in fouriers:
                model = Prophet(weekly_seasonality=False ,changepoint_prior_scale=cp, seasonality_mode=mode, n_changepoints= n)
                model.add_seasonality(name='weekly', period=7, fourier_order=f)
                model.fit(train)
                
                train_pred = model.predict(train[['ds']])
                rmse_train = np.sqrt(mean_squared_error(train['y'], train_pred['yhat']))
                relative_error_train=(rmse_train / train['y'].mean()) * 100
                future = model.make_future_dataframe(periods=10)
                forecast = model.predict(future)
                y_pred_test = forecast[['ds', 'yhat']].set_index('ds').loc[test['ds']]
                rmse_test = np.sqrt(mean_squared_error(test['y'], y_pred_test['yhat']))
                
                
                relative_error_test = (rmse_test / test['y'].mean()) * 100
                
                results.append({
                    'changepoint_prior_scale': cp,
                    'seasonality_mode': mode,
                    'n_changepoints':n,
                    'forier_id':f,
                    'relative_error_train%':relative_error_train,
                    'relative_rmse_test_%': relative_error_test
                })

df_results = pd.DataFrame(results).sort_values(by='relative_rmse_test_%')
print(df_results)

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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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01:35:06 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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01:35:07 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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01:35:08 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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01:35:10 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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01:35:11 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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01:35:12 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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01:35:13 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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01:35:15 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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01:35:17 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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01:35:19 - cmdstanpy - ERROR - Chain [1] error: error during processing Operation not permitted
Optimization terminated abnormally. Falling back to Newton.
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Optimization terminated abnormally. Falling back to Newton.
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     changepoint_prior_scale seasonality_mode  n_changepoints  forier_id  \
147                    0.500   multiplicative              60         20   
183                    0.800   multiplicative              50         20   
143                    0.500   multiplicative              50         20   
193                    0.800   multiplicative              80         10   
182                    0.800   multiplicative              50         15   
..                       ...              ...             ...        ...   
17                     0.001         additive              90         10   
13                     0.001         additive              80         10   
9                      0.001         additive              70         10   
5                      0.001         additive              60         10   
1                      0.001         additive              50         10   

     relative_error_train%  relative_rmse_test_%  
147              14.631177             48.962816  
183              14.380187             49.050205  
143              14.628050             49.124727  
193              14.561205             49.147330  
182              14.413348             49.172578  
..                     ...                   ...  
17               24.644042            176.659975  
13               24.644099            176.664969  
9                24.644324            176.686056  
5                24.644508            176.703124  
1                24.644568            176.708461  

[240 rows x 6 columns]

best_cp = 0.8
best_mode = 'multiplicative'
best_n_changepoint=90
best_f=15
model = Prophet(weekly_seasonality=False,changepoint_prior_scale=best_cp, seasonality_mode=best_mode,n_changepoints=best_n_changepoint)
model.add_seasonality(name='weekly', period=7, fourier_order=f)
model.fit(df_prophet)

future = model.make_future_dataframe(periods=10)
forecast = model.predict(future)

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=df_prophet['ds'],
    y=df_prophet['y'],
    mode='markers',
    name='Original Data',
    marker=dict(color='blue')
))

fig.add_trace(go.Scatter(
    x=forecast['ds'],
    y=forecast['yhat'],
    mode='lines',
    name='Forecast',
    line=dict(color='red')
))

# fig.add_trace(go.Scatter(
#     x=pd.concat([forecast['ds'], forecast['ds'][::-1]]),
#     y=pd.concat([forecast['yhat_upper'], forecast['yhat_lower'][::-1]]),
#     fill='toself',
#     fillcolor='rgba(255,0,0,0.2)',
#     line=dict(color='rgba(255,255,255,0)'),
#     hoverinfo="skip",
#     showlegend=True,
#     name='Confidence Interval'
# ))

# fig.update_layout(
#     title=f'The best Forecast with Tuned Parameters (CP={best_cp}, Mode={best_mode})',
#     xaxis_title='Date',
#     yaxis_title='Value',
#     template='plotly_white'
# )

# fig.show()
image_path = r"C:\Users\ASUS\OneDrive\Desktop\images\Screenshot 2025-06-20 192921.png"
image = Image.open(image_path)
display(image)

17:14:36 - cmdstanpy - INFO - Chain [1] start processing
17:14:36 - cmdstanpy - INFO - Chain [1] done processing

metrics_results = []

for cp in tqdm(changepoint_values):
    for mode in seasonality_modes:
        model = Prophet(weekly_seasonality=False,changepoint_prior_scale=cp, seasonality_mode=mode,n_changepoints=n)
        model.add_seasonality(name='weekly', period=7, fourier_order=f)

        model.fit(train)
        future = model.make_future_dataframe(periods=10)
        forecast = model.predict(future)

        y_pred = forecast[['ds', 'yhat']].set_index('ds').loc[test['ds']]
        y_true = test.set_index('ds')['y']

        mae = mean_absolute_error(y_true, y_pred['yhat'])
        rmse = np.sqrt(mean_squared_error(y_true, y_pred['yhat']))
        mape = np.mean(np.abs((y_true - y_pred['yhat']) / y_true)) * 100
        r2 = r2_score(y_true, y_pred['yhat'])

        mean_y = y_true.mean()
        mae_pct = (mae / mean_y) * 100
        rmse_pct = (rmse / mean_y) * 100
        r2_pct = r2 * 100

        metrics_results.append({
            'model': f'Prophet_cp={cp}_mode={mode}',
            'MAE (%)': mae_pct,
            'RMSE (%)': rmse_pct,
            'MAPE (%)': mape,
            'R2 Score (%)': r2_pct
        })

y_true_baseline = test['y'].values
y_pred_baseline = test['y'].shift(1).fillna(method='bfill').values

baseline_mae = mean_absolute_error(y_true_baseline, y_pred_baseline)
baseline_rmse = np.sqrt(mean_squared_error(y_true_baseline, y_pred_baseline))
baseline_mape = np.mean(np.abs((y_true_baseline - y_pred_baseline) / y_true_baseline)) * 100
baseline_r2 = r2_score(y_true_baseline, y_pred_baseline)

mean_y_baseline = y_true_baseline.mean()
baseline_mae_pct = (baseline_mae / mean_y_baseline) * 100
baseline_rmse_pct = (baseline_rmse / mean_y_baseline) * 100
baseline_r2_pct = baseline_r2 * 100

metrics_results.append({
    'model': 'Baseline (Previous Day)',
    'MAE (%)': baseline_mae_pct,
    'RMSE (%)': baseline_rmse_pct,
    'MAPE (%)': baseline_mape,
    'R2 Score (%)': baseline_r2_pct
})

df_all_results = pd.DataFrame(metrics_results).sort_values(by='RMSE (%)')
print(df_all_results)

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                                   model     MAE (%)    RMSE (%)    MAPE (%)  \
12               Baseline (Previous Day)   25.717051   31.229423   26.646085   
9     Prophet_cp=0.8_mode=multiplicative   44.241594   49.186542   55.134665   
7     Prophet_cp=0.5_mode=multiplicative   44.356000   49.353379   55.339817   
11    Prophet_cp=0.9_mode=multiplicative   44.354207   49.355102   55.337866   
5     Prophet_cp=0.3_mode=multiplicative   44.743061   49.980824   56.081909   
10          Prophet_cp=0.9_mode=additive   48.694533   56.091710   62.677341   
8           Prophet_cp=0.8_mode=additive   49.080165   56.539679   63.235939   
3     Prophet_cp=0.1_mode=multiplicative   50.864891   58.231680   65.737525   
6           Prophet_cp=0.5_mode=additive   50.552220   58.296615   65.372304   
4           Prophet_cp=0.3_mode=additive   53.667848   61.743497   69.612956   
2           Prophet_cp=0.1_mode=additive   62.791544   70.621015   80.886917   
1   Prophet_cp=0.001_mode=multiplicative  166.698878  170.913082  198.125518   
0         Prophet_cp=0.001_mode=additive  169.286963  173.184711  200.656321   

    R2 Score (%)  
12     13.208652  
9    -115.298549  
7    -116.761575  
11   -116.776708  
5    -122.308124  
10   -179.992161  
8    -184.482256  
3    -201.763809  
6    -202.437191  
4    -239.258676  
2    -343.829766  
1   -2499.555676  
0   -2569.116997

forecast

bins = [1,2 ,3,3.95,4, 5, 7, 10, 15, 30, 60, 120]
labels = [
    '1–2 $',
    '2–3 $',
    '3–3.95 $',
     '3.95–4 $',
    '4–5 $',
    '5–7 $',
    '7–10 $',
    '10–15 $',
    '15–30 $',
    '30–60 $',
    '1–2 hr (60–120 $)',

]

df['total_category'] = pd.cut(df['total_cost'], bins=bins, labels=labels, right=False)

print(df['total_category'].value_counts())

total_category
3.95–4 $             3011977
4–5 $                1147289
1–2 $                 717707
2–3 $                 677682
3–3.95 $              304753
5–7 $                 129323
7–10 $                 60219
10–15 $                29250
15–30 $                15738
1–2 hr (60–120 $)       6010
30–60 $                 3794
Name: count, dtype: int64

def cost_category(cost):
    if cost < 3:
        return  3
    elif cost < 7:
        return  2 
    else:
        return 1 

df['cost_category'] = df['total_cost'].apply(cost_category)

locations_df['NAME'] = locations_df['NAME'].str.strip().str.lower()
df['start_station_name'] = df['start_station_name'].str.strip().str.lower()

capacity_bin_map = locations_df.set_index('NAME')['CAPACITY_BIN'].to_dict()

df['start_capacity_bin'] = df['start_station_name'].map(capacity_bin_map)
df['start_capacity_bin']

0           Small
1           Small
2           Small
3           Small
4           Small
            ...  
6103737    Medium
6103738    Medium
6103739    Medium
6103740    Medium
6103741    Medium
Name: start_capacity_bin, Length: 6103742, dtype: object

df['start_date'] = pd.to_datetime(df['started_at'].dt.date)

df['start_date'] = pd.to_datetime(df['start_date'])
weather_df['date'] = pd.to_datetime(weather_df['date'])
df = df.drop(columns=[col for col in ['date', 'weather_bin'] if col in df.columns])

df_merge = df.merge(
    weather_df[['date', 'temp', 'humidity', 'windspeed', 'weather_bin']],
    how='left',
    left_on='start_date',
    right_on='date'
)
df_merge['start_capacity_bin'] = df_merge['start_station_name'].map(capacity_bin_map)

df_sample = df_merge.dropna(subset=[
    'rideable_type',
    'member_casual',
    'cost_category',
    'temp',
    'humidity',
    'windspeed',
    'weather_bin',
    'distance_to_business_area_x',
    'start_distance_to_shuttle_m',
    'start_distance_to_metro_m',
  
    'start_capacity_bin'
,
'start_geohash',
'end_geohash'
])

samples_per_group = 500

grouped_sample = (
    df_sample
    .groupby([
        'rideable_type',
        'member_casual',
        'weather_bin',
    ], group_keys=False)
    .apply(lambda x: x.sample(min(len(x), samples_per_group), random_state=42))
)

print(" حجم العينة النهائي:", grouped_sample.shape[0])

 حجم العينة النهائي: 6000

numeric_features = [
    'temp', 'humidity', 'windspeed', 'ride_duration_min',
    'cost_category', 'distance_to_business_area_x',
    'start_distance_to_shuttle_m', 'start_distance_to_metro_m'
]

categorical_features = [
    'rideable_type', 'member_casual',
    'weather_bin', 'start_geohash','end_geohash', 'start_capacity_bin'
]

all_features = numeric_features + categorical_features
grouped_sample = grouped_sample.dropna(subset=all_features)

grouped_sample[categorical_features] = grouped_sample[categorical_features].astype(str)

preprocessor = ColumnTransformer(transformers=[
    ('num', StandardScaler(), numeric_features),
    ('cat', OneHotEncoder(drop='first', sparse_output=False), categorical_features)
])

X_processed = preprocessor.fit_transform(grouped_sample)

k = 3
kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
kmeans_labels = kmeans.fit_predict(X_processed)
sil_score = silhouette_score(X_processed, kmeans_labels)
print(f"KMeans - Silhouette Score = {sil_score:.4f}")
grouped_sample['kmeans_cluster'] = kmeans_labels

KMeans - Silhouette Score = 0.1301

k = 3
kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
kmeans_labels = kmeans.fit_predict(X_processed)
algo_name = "KMeans"

sil_score = silhouette_score(X_processed, kmeans_labels)
print(f"KMeans - Silhouette Score = {sil_score:.4f}")

grouped_sample['kmeans_cluster'] = kmeans_labels
print(f"\n--- Cluster Description for {algo_name} ---")

print("\nAverage Values of Numerical Features per Cluster:")
numerical_summary = grouped_sample.groupby('kmeans_cluster')[numeric_features].mean()
print(numerical_summary)
print("\nDistribution of Categorical Features per Cluster:")
for feature in categorical_features:
    print(f"\nFeature: {feature}")
    cluster_counts = grouped_sample.groupby(['kmeans_cluster', feature]).size().unstack(fill_value=0)
    cluster_percentages = cluster_counts.apply(lambda x: x / x.sum(), axis=1) * 100
    print(cluster_percentages.round(2)) 

print("\nCluster Sizes:")
print(grouped_sample['kmeans_cluster'].value_counts().sort_index())

pca = PCA(n_components=2)
components = pca.fit_transform(X_processed)
pca_df = pd.DataFrame(data = components, columns = ['PC1', 'PC2'])
pca_df['kmeans_cluster'] = kmeans_labels
pca_df['kmeans_cluster'] = pca_df['kmeans_cluster'].astype(str) 

fig = px.scatter(
    pca_df,
    x='PC1',
    y='PC2',
    color='kmeans_cluster',
    title='KMeans Clustering (PCA Reduced Dimensions)',
    labels={'PC1': 'Principal Component 1', 'PC2': 'Principal Component 2'},
    hover_data={'PC1':False, 'PC2':False, 'kmeans_cluster':True} 

centroids_pca = pca.transform(kmeans.cluster_centers_)
fig.add_trace(go.Scatter(
    x=centroids_pca[:, 0], y=centroids_pca[:, 1],
    mode='markers',
    marker=dict(size=15, symbol='x', color='black'),
    name='Centroids'
))

#fig.show(renderer="browser")

image_path = r'C:\Users\asus\Desktop\5\2\DM\Homework\نتائج\Screenshot 2025-06-20 185735.png'

image = Image.open(image_path)
display(image)

processed_feature_names = numeric_features[:]
ohe_feature_names = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features)
processed_feature_names.extend(ohe_feature_names)

loadings = pd.DataFrame(pca.components_.T, columns=['PC1', 'PC2'], index=processed_feature_names)
print("\nPCA Loadings (Contribution of Original Features to Principal Components):")
print(loadings.to_string())

KMeans - Silhouette Score = 0.1301

--- Cluster Description for KMeans ---

Average Values of Numerical Features per Cluster:
                     temp   humidity  windspeed  ride_duration_min  \
kmeans_cluster                                                       
0               14.642857  65.783117  20.644156          43.850649   
1               14.171036  64.839036  20.861928          11.192578   
2               14.495582  65.109511  21.502885          16.962084   

                cost_category  distance_to_business_area_x  \
kmeans_cluster                                               
0                    2.506494                     0.230632   
1                    3.000000                     0.016970   
2                    1.967516                     0.014864   

                start_distance_to_shuttle_m  start_distance_to_metro_m  
kmeans_cluster                                                          
0                              21578.148022                1033.338398  
1                               1170.632474                 125.137413  
2                                987.248651                 115.293326  

Distribution of Categorical Features per Cluster:

Feature: rideable_type
rideable_type   classic_bike  electric_bike
kmeans_cluster                             
0                      74.03          25.97
1                      58.36          41.64
2                      45.01          54.99

Feature: member_casual
member_casual   casual  member
kmeans_cluster                
0                72.73   27.27
1               100.00    0.00
2                22.58   77.42

Feature: weather_bin
weather_bin     Cloudy  Rainy  Sunny
kmeans_cluster                      
0                37.66  29.87  32.47
1                35.33  31.90  32.77
2                32.17  34.17  33.65

Feature: start_geohash
start_geohash   dqbvrzn  dqbvx3p  dqbvx7w  dqbvx96  dqbvxc8  dqbvxe9  dqbvxek  \
kmeans_cluster                                                                  
0                   1.3      2.6      1.3      1.3      2.6      1.3     5.19   
1                   0.0      0.0      0.0      0.0      0.0      0.0     0.00   
2                   0.0      0.0      0.0      0.0      0.0      0.0     0.00   

start_geohash   dqbvxeu  dqbvxfe  dqbvxgd  ...  dqcnj5x  dqcnk9e  dqcnke2  \
kmeans_cluster                             ...                              
0                   1.3      2.6      1.3  ...     0.00      1.3      1.3   
1                   0.0      0.0      0.0  ...     0.05      0.0      0.0   
2                   0.0      0.0      0.0  ...     0.00      0.0      0.0   

start_geohash   dqcnkjn  dqcnndr  dqcnndt  dqcnnsm  dqcns19  dqcns48  dqcq35j  
kmeans_cluster                                                                 
0                   3.9     0.00     0.00     0.00      1.3      1.3      1.3  
1                   0.0     0.00     0.14     0.10      0.0      0.0      0.0  
2                   0.0     0.03     0.03     0.03      0.0      0.0      0.0  

[3 rows x 606 columns]

Feature: end_geohash
end_geohash     dqbvx7w  dqbvxdr  dqbvxe9  dqbvxek  dqbvxeu  dqbvxfe  dqbvxgd  \
kmeans_cluster                                                                  
0                   1.3      1.3      1.3      1.3      1.3      1.3      2.6   
1                   0.0      0.0      0.0      0.0      0.0      0.0      0.0   
2                   0.0      0.0      0.0      0.0      0.0      0.0      0.0   

end_geohash     dqbvxsm  dqbvxsw  dqbvxv0  ...  dqcnk9e  dqcnke2  dqcnksb  \
kmeans_cluster                             ...                              
0                   2.6      1.3      2.6  ...      1.3      1.3      1.3   
1                   0.0      0.0      0.0  ...      0.0      0.0      0.0   
2                   0.0      0.0      0.0  ...      0.0      0.0      0.0   

end_geohash     dqcnn9r  dqcnndt  dqcnnee  dqcnnez  dqcnnsm  dqcns1f  dqcq349  
kmeans_cluster                                                                 
0                   0.0     0.00      0.0     0.00     0.00      1.3      1.3  
1                   0.1     0.00      0.1     0.00     0.05      0.0      0.0  
2                   0.0     0.05      0.0     0.03     0.00      0.0      0.0  

[3 rows x 608 columns]

Feature: start_capacity_bin
start_capacity_bin  Large  Medium  Small
kmeans_cluster                          
0                    0.00   24.68  75.32
1                   14.89   62.22  22.89
2                   17.18   61.72  21.10

Cluster Sizes:
kmeans_cluster
0      77
1    2075
2    3848
Name: count, dtype: int64

PCA Loadings (Contribution of Original Features to Principal Components):
                                  PC1       PC2
temp                         0.014190  0.383385
humidity                    -0.025208 -0.529767
windspeed                    0.005260  0.625620
ride_duration_min            0.072271  0.250989
cost_category                0.065864 -0.264844
distance_to_business_area_x  0.608527 -0.026624
start_distance_to_shuttle_m  0.621779 -0.025708
start_distance_to_metro_m    0.470708  0.020723
rideable_type_electric_bike -0.014255  0.002430
member_casual_member        -0.041230  0.060262
weather_bin_Rainy           -0.007257 -0.009168
weather_bin_Sunny            0.013008  0.204831
start_geohash_dqbvx3p        0.002388 -0.000364
start_geohash_dqbvx7w        0.001077  0.000072
start_geohash_dqbvx96        0.001233  0.000016
start_geohash_dqbvxc8        0.002127 -0.000408
start_geohash_dqbvxe9        0.001189  0.000014
start_geohash_dqbvxek        0.004823 -0.000160
start_geohash_dqbvxeu        0.001211  0.000230
start_geohash_dqbvxfe        0.002009  0.000190
start_geohash_dqbvxgd        0.001046 -0.000209
start_geohash_dqbvxs5        0.001225 -0.000009
start_geohash_dqbvxsm        0.002451 -0.000089
start_geohash_dqbvxsw        0.002386 -0.000160
start_geohash_dqbvxts        0.002621 -0.000035
start_geohash_dqbvxv0        0.001188 -0.000194
start_geohash_dqbvxzb        0.002805  0.000118
start_geohash_dqchbuk        0.000767 -0.000093
start_geohash_dqchbv1        0.000797 -0.000110
start_geohash_dqchby0        0.000656 -0.000093
start_geohash_dqchbyf        0.000708 -0.000230
start_geohash_dqchtzv        0.000674  0.000129
start_geohash_dqchuch        0.000314  0.000041
start_geohash_dqchv1g        0.000254  0.000097
start_geohash_dqchv37        0.000232  0.000239
start_geohash_dqchv64        0.000241 -0.000244
start_geohash_dqchv8b        0.000254  0.000367
start_geohash_dqchvu3        0.000198 -0.000263
start_geohash_dqchvue        0.000179 -0.000011
start_geohash_dqchvwn        0.000160  0.000147
start_geohash_dqchvy9        0.000140  0.000097
start_geohash_dqchvz1        0.000710  0.000127
start_geohash_dqchvz7        0.000138  0.000323
start_geohash_dqchwpv        0.000493  0.000152
start_geohash_dqchwr8        0.000399 -0.000013
start_geohash_dqchwrg        0.000431 -0.000235
start_geohash_dqchwwg        0.000277 -0.000018
start_geohash_dqchwwn        0.002209 -0.000927
start_geohash_dqchwwy        0.000408 -0.000037
start_geohash_dqchwxr        0.000205 -0.000157
start_geohash_dqchwz3        0.000266 -0.000058
start_geohash_dqchwzs        0.000342  0.000042
start_geohash_dqchwzv        0.000298 -0.000033
start_geohash_dqchxkn        0.000801 -0.000120
start_geohash_dqchxky        0.001547  0.000169
start_geohash_dqchxyb        0.000459 -0.000093
start_geohash_dqchy2q        0.000590 -0.000352
start_geohash_dqchy2w        0.000632  0.000063
start_geohash_dqchy3t        0.000585 -0.000120
start_geohash_dqchy6u        0.000512 -0.000334
start_geohash_dqchy83        0.000483 -0.000102
start_geohash_dqchy87        0.000309 -0.000009
start_geohash_dqchy8m        0.000735 -0.000030
start_geohash_dqchy9g        0.001729 -0.000404
start_geohash_dqchy9t        0.000158 -0.000135
start_geohash_dqchy9y        0.000214  0.000045
start_geohash_dqchyb1        0.001132  0.000665
start_geohash_dqchyb4        0.000761 -0.000040
start_geohash_dqchyc3        0.000537  0.000372
start_geohash_dqchycg        0.000422 -0.000210
start_geohash_dqchydb        0.000492 -0.000098
start_geohash_dqchydx        0.000153 -0.000193
start_geohash_dqchye5        0.001027 -0.000101
start_geohash_dqchyeq        0.000298 -0.000006
start_geohash_dqchyer        0.000191  0.000182
start_geohash_dqchykp        0.000150 -0.000072
start_geohash_dqchymq        0.000303 -0.000236
start_geohash_dqchypz        0.000365  0.000366
start_geohash_dqchyqu        0.000091  0.000072
start_geohash_dqchyr5        0.000086  0.000062
start_geohash_dqchysx        0.000162 -0.000180
start_geohash_dqchytn        0.000582  0.000115
start_geohash_dqchytr        0.000234 -0.000250
start_geohash_dqchyw0        0.000490 -0.000487
start_geohash_dqchyxj        0.000358  0.000133
start_geohash_dqchyxm        0.000361  0.000188
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end_geohash_dqcm0kx          0.000104  0.000020
end_geohash_dqcm0mx          0.000229  0.000343
end_geohash_dqcm0n3         -0.000089  0.000312
end_geohash_dqcm0n7          0.000061  0.000002
end_geohash_dqcm0nb         -0.000117 -0.000083
end_geohash_dqcm0ns         -0.000149  0.000561
end_geohash_dqcm0pf         -0.000167 -0.000127
end_geohash_dqcm0pn         -0.000040  0.000274
end_geohash_dqcm0pw         -0.000072  0.000234
end_geohash_dqcm0q2          0.000014  0.000192
end_geohash_dqcm0qf          0.000010  0.000430
end_geohash_dqcm0re          0.000007 -0.000074
end_geohash_dqcm0xy          0.000005 -0.000057
end_geohash_dqcm1r8          0.000031 -0.000123
end_geohash_dqcm1z3          0.000154 -0.000160
end_geohash_dqcm20e         -0.000217  0.000005
end_geohash_dqcm20q         -0.000085  0.000064
end_geohash_dqcm21d         -0.000402 -0.000666
end_geohash_dqcm21n         -0.000085 -0.000147
end_geohash_dqcm21w         -0.000034  0.000267
end_geohash_dqcm22s         -0.000082 -0.000027
end_geohash_dqcm231          0.000032 -0.000583
end_geohash_dqcm23c         -0.000066  0.000107
end_geohash_dqcm245         -0.000160 -0.000246
end_geohash_dqcm24g         -0.000149 -0.000218
end_geohash_dqcm24w         -0.000026 -0.000084
end_geohash_dqcm25s         -0.000051  0.000492
end_geohash_dqcm27y          0.000102  0.000033
end_geohash_dqcm28c          0.000024  0.000310
end_geohash_dqcm28z          0.000117 -0.000068
end_geohash_dqcm290          0.000057 -0.000051
end_geohash_dqcm2hq          0.000123 -0.001087
end_geohash_dqcm2ju          0.000002  0.000142
end_geohash_dqcm2n8         -0.000031 -0.000205
end_geohash_dqcm2p8          0.000008 -0.000272
end_geohash_dqcm2v6          0.000026  0.000245
end_geohash_dqcm2w5          0.000018 -0.000168
end_geohash_dqcm302          0.000095  0.000294
end_geohash_dqcm337          0.000126  0.000121
end_geohash_dqcm5z9          0.000428  0.000024
end_geohash_dqcm737          0.000441  0.000312
end_geohash_dqcm813          0.000029 -0.000104
end_geohash_dqcm839          0.000049 -0.000020
end_geohash_dqcm88p          0.000109  0.000112
end_geohash_dqcm8be          0.000175  0.000328
end_geohash_dqcm8e9          0.000381 -0.000437
end_geohash_dqcm8er          0.000096  0.000088
end_geohash_dqcm8v7          0.000457  0.000003
end_geohash_dqcm95t          0.000114  0.000024
end_geohash_dqcm9h3          0.000272 -0.000208
end_geohash_dqcm9kc          0.000503  0.000234
end_geohash_dqcm9np          0.000163  0.000069
end_geohash_dqcm9ry          0.000204 -0.000047
end_geohash_dqcmb1d          0.000113  0.000025
end_geohash_dqcmfeg          0.000404  0.000168
end_geohash_dqcn7y3          0.001089 -0.000145
end_geohash_dqcnj5x          0.000150  0.000155
end_geohash_dqcnjhy          0.000353 -0.000212
end_geohash_dqcnjk2          0.001065 -0.000245
end_geohash_dqcnk7p          0.000478  0.000072
end_geohash_dqcnk9e          0.000494 -0.000018
end_geohash_dqcnke2          0.000476 -0.000205
end_geohash_dqcnksb          0.000480 -0.000036
end_geohash_dqcnn9r          0.000499 -0.000446
end_geohash_dqcnndt          0.000433 -0.000160
end_geohash_dqcnnee          0.000574 -0.000046
end_geohash_dqcnnez          0.000258  0.000080
end_geohash_dqcnnsm          0.000307 -0.000066
end_geohash_dqcns1f          0.000688  0.000061
end_geohash_dqcq349          0.000460 -0.000229
start_capacity_bin_Medium   -0.048515  0.006878
start_capacity_bin_Small     0.077625 -0.003495

dbscan = DBSCAN(eps=1.5, min_samples=3)
cluster_labels = dbscan.fit_predict(X_processed)
algo_name = "DBSCAN"


grouped_sample['cluster_label'] = cluster_labels
grouped_sample['cluster_label'] = grouped_sample['cluster_label'].astype(str) # لتحويل -1 في DBSCAN إلى '-1' نصية
unique_labels = set(cluster_labels)
clusters_for_silhouette = [label for label in unique_labels if label != -1]

if len(clusters_for_silhouette) > 1:
    valid_indices = (cluster_labels != -1)
    sil_score = silhouette_score(X_processed[valid_indices], cluster_labels[valid_indices])
    print(f"{algo_name} - Silhouette Score (excluding outliers) = {sil_score:.4f}")
else:
    print(f"{algo_name} - عدد العناقيد غير كافٍ أو معظمها outliers، لا يمكن حساب Silhouette Score.")



print(f"\n--- Cluster Description for {algo_name} ---")

print("\nAverage Values of Numerical Features per Cluster:")
numerical_summary = grouped_sample.groupby('cluster_label')[numeric_features].mean()
print(numerical_summary)

print("\nDistribution of Categorical Features per Cluster:")
for feature in categorical_features:
    print(f"\nFeature: {feature}")
    cluster_counts = grouped_sample.groupby(['cluster_label', feature]).size().unstack(fill_value=0)
    cluster_percentages = cluster_counts.apply(lambda x: x / x.sum(), axis=1) * 100
    print(cluster_percentages.round(2)) 

print("\nCluster Sizes:")
print(grouped_sample['cluster_label'].value_counts().sort_index())

pca = PCA(n_components=2)
components = pca.fit_transform(X_processed)

pca_df = pd.DataFrame(data = components, columns = ['PC1', 'PC2'])
pca_df['cluster_label'] = grouped_sample['cluster_label']

fig = px.scatter(
    pca_df,
    x='PC1',
    y='PC2',
    color='cluster_label',
    title=f'{algo_name} Clustering (PCA Reduced Dimensions)',
    labels={'PC1': 'Principal Component 1', 'PC2': 'Principal Component 2'}
)

# fig.add_trace(go.Scatter(
#     x=centroids_pca[:, 0], y=centroids_pca[:, 1],
#     mode='markers',
#     marker=dict(size=15, symbol='x', color='black'),
#     name='Centroids'
# ))

#fig.show()
image_path = r"C:\Users\ASUS\OneDrive\Desktop\images\Screenshot 2025-06-20 185455.png"
image = Image.open(image_path)
display(image)

processed_feature_names = numeric_features[:]
preprocessor.fit(grouped_sample) 
ohe_feature_names = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features)
processed_feature_names.extend(ohe_feature_names)

loadings = pd.DataFrame(pca.components_.T, columns=['PC1', 'PC2'], index=processed_feature_names)
print("\nPCA Loadings (Contribution of Original Features to Principal Components):")
print(loadings.to_string())

DBSCAN - Silhouette Score (excluding outliers) = -0.1635

--- Cluster Description for DBSCAN ---

Average Values of Numerical Features per Cluster:
                    temp   humidity  windspeed  ride_duration_min  \
cluster_label                                                       
-1             16.305688  63.226946  23.412787          21.301977   
0              11.779218  66.864425  17.979218          10.813570   
1               5.182609  67.207246  12.137681           9.892754   
10              5.040000  67.925000  11.975000           9.400000   
100            10.166667  40.266667  28.966667           4.200000   
...                  ...        ...        ...                ...   
95             25.233333  53.600000  22.966667          10.900000   
96             22.133333  52.600000  27.233333           9.400000   
97             25.700000  58.233333  22.366667           5.166667   
98             14.880000  49.560000  26.240000           9.180000   
99             24.500000  55.633333  20.933333           9.600000   

               cost_category  distance_to_business_area  \
cluster_label                                             
-1                  2.302541                   0.030351   
0                   3.000000                   0.008439   
1                   3.000000                   0.011699   
10                  3.000000                   0.006574   
100                 2.000000                   0.040535   
...                      ...                        ...   
95                  2.000000                   0.080951   
96                  2.000000                   0.010884   
97                  2.000000                   0.014410   
98                  2.000000                   0.009703   
99                  2.000000                   0.018383   

               start_distance_to_shuttle_m  start_distance_to_metro_m  
cluster_label                                                          
-1                             2304.793950                 170.948676  
0                               554.463299                  84.665590  
1                               510.036236                  68.580518  
10                              418.148358                 288.865420  
100                             249.156334                  31.869308  
...                                    ...                        ...  
95                             5373.717928                  17.009052  
96                              917.693840                  63.428558  
97                             1650.659496                 147.881532  
98                             1511.695479                 153.192424  
99                             1095.261916                  25.830314  

[167 rows x 8 columns]

Distribution of Categorical Features per Cluster:

Feature: rideable_type
rideable_type  classic_bike  electric_bike
cluster_label                             
-1                    48.73          51.27
0                     60.51          39.49
1                     49.28          50.72
10                    50.00          50.00
100                  100.00           0.00
...                     ...            ...
95                    66.67          33.33
96                    66.67          33.33
97                    66.67          33.33
98                    40.00          60.00
99                    66.67          33.33

[167 rows x 2 columns]

Feature: member_casual
member_casual  casual  member
cluster_label                
-1              59.42   40.58
0              100.00    0.00
1              100.00    0.00
10             100.00    0.00
100              0.00  100.00
...               ...     ...
95               0.00  100.00
96               0.00  100.00
97               0.00  100.00
98              20.00   80.00
99               0.00  100.00

[167 rows x 2 columns]

Feature: weather_bin
weather_bin    Cloudy  Rainy   Sunny
cluster_label                       
-1              13.92  42.28   43.81
0               57.82  21.52   20.66
1               98.55   0.00    1.45
10              95.00   0.00    5.00
100              0.00   0.00  100.00
...               ...    ...     ...
95               0.00   0.00  100.00
96               0.00   0.00  100.00
97               0.00   0.00  100.00
98               0.00   0.00  100.00
99               0.00   0.00  100.00

[167 rows x 3 columns]

Feature: start_geohash
start_geohash  dqbvrzn  dqbvx3p  dqbvx7w  dqbvx96  dqbvxc8  dqbvxe9  dqbvxek  \
cluster_label                                                                  
-1                0.04     0.08     0.04     0.04     0.08     0.04     0.16   
0                 0.00     0.00     0.00     0.00     0.00     0.00     0.00   
1                 0.00     0.00     0.00     0.00     0.00     0.00     0.00   
10                0.00     0.00     0.00     0.00     0.00     0.00     0.00   
100               0.00     0.00     0.00     0.00     0.00     0.00     0.00   
...                ...      ...      ...      ...      ...      ...      ...   
95                0.00     0.00     0.00     0.00     0.00     0.00     0.00   
96                0.00     0.00     0.00     0.00     0.00     0.00     0.00   
97                0.00     0.00     0.00     0.00     0.00     0.00     0.00   
98                0.00     0.00     0.00     0.00     0.00     0.00     0.00   
99                0.00     0.00     0.00     0.00     0.00     0.00     0.00   

start_geohash  dqbvxeu  dqbvxfe  dqbvxgd  ...  dqcnj5x  dqcnk9e  dqcnke2  \
cluster_label                             ...                              
-1                0.04     0.08     0.04  ...     0.04     0.04     0.04   
0                 0.00     0.00     0.00  ...     0.00     0.00     0.00   
1                 0.00     0.00     0.00  ...     0.00     0.00     0.00   
10                0.00     0.00     0.00  ...     0.00     0.00     0.00   
100               0.00     0.00     0.00  ...     0.00     0.00     0.00   
...                ...      ...      ...  ...      ...      ...      ...   
95                0.00     0.00     0.00  ...     0.00     0.00     0.00   
96                0.00     0.00     0.00  ...     0.00     0.00     0.00   
97                0.00     0.00     0.00  ...     0.00     0.00     0.00   
98                0.00     0.00     0.00  ...     0.00     0.00     0.00   
99                0.00     0.00     0.00  ...     0.00     0.00     0.00   

start_geohash  dqcnkjn  dqcnndr  dqcnndt  dqcnnsm  dqcns19  dqcns48  dqcq35j  
cluster_label                                                                 
-1                0.12     0.04     0.08     0.12     0.04     0.04     0.04  
0                 0.00     0.00     0.00     0.00     0.00     0.00     0.00  
1                 0.00     0.00     0.00     0.00     0.00     0.00     0.00  
10                0.00     0.00     0.00     0.00     0.00     0.00     0.00  
100               0.00     0.00     0.00     0.00     0.00     0.00     0.00  
...                ...      ...      ...      ...      ...      ...      ...  
95                0.00     0.00     0.00     0.00     0.00     0.00     0.00  
96                0.00     0.00     0.00     0.00     0.00     0.00     0.00  
97                0.00     0.00     0.00     0.00     0.00     0.00     0.00  
98                0.00     0.00     0.00     0.00     0.00     0.00     0.00  
99                0.00     0.00     0.00     0.00     0.00     0.00     0.00  

[167 rows x 606 columns]

Feature: start_capacity_bin
start_capacity_bin  10–15 $  15–30 $  30–60 $  7–10 $
cluster_label                                        
-1                    34.85    53.65    11.25    0.24
0                      0.00    89.73    10.27    0.00
1                    100.00     0.00     0.00    0.00
10                   100.00     0.00     0.00    0.00
100                  100.00     0.00     0.00    0.00
...                     ...      ...      ...     ...
95                     0.00   100.00     0.00    0.00
96                     0.00     0.00   100.00    0.00
97                     0.00   100.00     0.00    0.00
98                     0.00     0.00   100.00    0.00
99                     0.00   100.00     0.00    0.00

[167 rows x 4 columns]

Cluster Sizes:
cluster_label
-1     2479
0       818
1        69
10       20
100       3
       ... 
95        3
96        6
97        3
98        5
99        3
Name: count, Length: 167, dtype: int64

PCA Loadings (Contribution of Original Features to Principal Components):
                                      PC1       PC2
temp                         1.473775e-02  0.383722
humidity                    -2.515442e-02 -0.529548
windspeed                    5.267178e-03  0.625646
ride_duration_min            7.275576e-02  0.250793
cost_category                6.636345e-02 -0.264933
distance_to_business_area    6.096214e-01 -0.026776
start_distance_to_shuttle_m  6.227339e-01 -0.025858
start_distance_to_metro_m    4.736822e-01  0.020366
rideable_type_electric_bike -1.449674e-02  0.002489
member_casual_member        -4.160469e-02  0.060349
weather_bin_Rainy           -7.180000e-03 -0.009047
weather_bin_Sunny            1.317847e-02  0.204815
start_geohash_dqbvx3p        2.398170e-03 -0.000365
start_geohash_dqbvx7w        1.081112e-03  0.000072
start_geohash_dqbvx96        1.238706e-03  0.000015
start_geohash_dqbvxc8        2.134598e-03 -0.000409
start_geohash_dqbvxe9        1.194380e-03  0.000014
start_geohash_dqbvxek        4.865869e-03 -0.000163
start_geohash_dqbvxeu        1.221426e-03  0.000229
start_geohash_dqbvxfe        2.015529e-03  0.000190
start_geohash_dqbvxgd        1.049133e-03 -0.000209
start_geohash_dqbvxs5        1.231048e-03 -0.000009
start_geohash_dqbvxsm        2.461707e-03 -0.000091
start_geohash_dqbvxsw        2.396488e-03 -0.000162
start_geohash_dqbvxts        2.633608e-03 -0.000037
start_geohash_dqbvxv0        1.193487e-03 -0.000195
start_geohash_dqbvxzb        2.820167e-03  0.000116
start_geohash_dqchbuk        7.681811e-04 -0.000093
start_geohash_dqchbv1        7.979695e-04 -0.000110
start_geohash_dqchby0        6.562643e-04 -0.000093
start_geohash_dqchbyf        7.080968e-04 -0.000231
start_geohash_dqchtzv        6.710258e-04  0.000129
start_geohash_dqchuch        3.118400e-04  0.000041
start_geohash_dqchv1g        2.512065e-04  0.000097
start_geohash_dqchv37        2.294478e-04  0.000239
start_geohash_dqchv64        2.386256e-04 -0.000244
start_geohash_dqchv8b        2.514165e-04  0.000367
start_geohash_dqchvu3        1.950884e-04 -0.000263
start_geohash_dqchvue        1.758171e-04 -0.000011
start_geohash_dqchvwn        1.566542e-04  0.000147
start_geohash_dqchvy9        1.371230e-04  0.000097
start_geohash_dqchvz1        6.943647e-04  0.000127
start_geohash_dqchvz7        1.345267e-04  0.000323
start_geohash_dqchwpv        4.885834e-04  0.000152
start_geohash_dqchwr8        3.941061e-04 -0.000014
start_geohash_dqchwrg        4.367925e-04 -0.000235
start_geohash_dqchwwg        2.745263e-04 -0.000018
start_geohash_dqchwwn        2.182080e-03 -0.000927
start_geohash_dqchwwy        4.133777e-04 -0.000037
start_geohash_dqchwxr        2.020046e-04 -0.000157
start_geohash_dqchwz3        2.641399e-04 -0.000058
start_geohash_dqchwzs        3.406984e-04  0.000042
start_geohash_dqchwzv        3.019624e-04 -0.000033
start_geohash_dqchxkn        7.947746e-04 -0.000120
start_geohash_dqchxky        1.538005e-03  0.000169
start_geohash_dqchxyb        4.542322e-04 -0.000093
start_geohash_dqchy2q        5.975283e-04 -0.000352
start_geohash_dqchy2w        6.242084e-04  0.000063
start_geohash_dqchy3t        5.814952e-04 -0.000120
start_geohash_dqchy6u        5.074584e-04 -0.000334
start_geohash_dqchy83        4.889999e-04 -0.000102
start_geohash_dqchy87        3.078158e-04 -0.000009
start_geohash_dqchy8m        7.288436e-04 -0.000030
start_geohash_dqchy9g        1.755974e-03 -0.000403
start_geohash_dqchy9t        1.599728e-04 -0.000135
start_geohash_dqchy9y        2.119949e-04  0.000045
start_geohash_dqchyb1        1.118963e-03  0.000665
start_geohash_dqchyb4        7.544982e-04 -0.000040
start_geohash_dqchyc3        5.287096e-04  0.000372
start_geohash_dqchycg        4.168210e-04 -0.000210
start_geohash_dqchydb        4.993339e-04 -0.000098
start_geohash_dqchydx        1.493128e-04 -0.000193
start_geohash_dqchye5        1.042088e-03 -0.000101
start_geohash_dqchyeq        3.027016e-04 -0.000005
start_geohash_dqchyer        1.880741e-04  0.000182
start_geohash_dqchykp        1.467629e-04 -0.000072
start_geohash_dqchymq        2.962826e-04 -0.000236
start_geohash_dqchypz        3.516381e-04  0.000366
start_geohash_dqchyqu        8.731297e-05  0.000072
start_geohash_dqchyr5        8.211161e-05  0.000062
start_geohash_dqchysx        1.640613e-04 -0.000180
start_geohash_dqchytn        5.914669e-04  0.000115
start_geohash_dqchytr        2.270930e-04 -0.000249
start_geohash_dqchyw0        4.976008e-04 -0.000487
start_geohash_dqchyxj        3.478416e-04  0.000133
start_geohash_dqchyxm        3.473857e-04  0.000189
start_geohash_dqchyxs        2.923656e-04 -0.000034
start_geohash_dqchzth        6.739836e-05  0.000151
start_geohash_dqcj0bk        7.473412e-04 -0.000294
start_geohash_dqcj1eu        5.718829e-04  0.000047
start_geohash_dqcj1se        1.169850e-03  0.000037
start_geohash_dqcj1tm        1.115998e-03 -0.000538
start_geohash_dqcj4ey        9.670979e-04 -0.000401
start_geohash_dqcj4s4        4.997773e-04 -0.000121
start_geohash_dqcj4sd        5.189617e-04  0.000010
start_geohash_dqcj4tb        5.234895e-04 -0.000004
start_geohash_dqcj4td        5.025708e-04 -0.000224
start_geohash_dqcj4v8        4.922787e-04 -0.000238
start_geohash_dqcj4wk        1.852601e-03  0.000106
start_geohash_dqcj5j3        4.267466e-04 -0.000131
start_geohash_dqcj5mk        3.585692e-04  0.000050
start_geohash_dqcj5vb        1.012884e-03  0.000476
start_geohash_dqcj5vv        3.185863e-04  0.000185
start_geohash_dqcj5xs        3.073348e-04 -0.000218
start_geohash_dqcj5yg        2.635110e-04 -0.000164
start_geohash_dqcj5ys        1.118831e-03 -0.000275
start_geohash_dqcj5z1        2.756931e-04 -0.000013
start_geohash_dqcj6em        9.891087e-04 -0.000097
start_geohash_dqcj6uz        4.694017e-04  0.000077
start_geohash_dqcj6v1        4.053152e-04 -0.000085
start_geohash_dqcj6v2        8.394460e-04 -0.000364
start_geohash_dqcj6y9        4.856870e-04  0.000037
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start_geohash_dqcjreg       -5.815801e-04 -0.000007
start_geohash_dqcjrew       -4.587242e-04  0.001034
start_geohash_dqcjrf5       -4.795231e-04  0.000055
start_geohash_dqcjrf8       -6.579631e-04 -0.000321
start_geohash_dqcjrfb       -4.202024e-04  0.000270
start_geohash_dqcjrfc       -1.357766e-03 -0.000884
start_geohash_dqcjrff       -1.059754e-03  0.000027
start_geohash_dqcjrfq       -1.015515e-04 -0.000149
start_geohash_dqcjrfu        2.024783e-05 -0.000927
start_geohash_dqcjrg8       -4.055040e-04  0.000273
start_geohash_dqcjrhc       -6.004489e-04 -0.000180
start_geohash_dqcjrhg       -1.561892e-03 -0.001110
start_geohash_dqcjrhn       -7.051932e-04  0.000579
start_geohash_dqcjrhs       -8.603771e-04  0.000028
start_geohash_dqcjrht       -5.267803e-04 -0.000363
start_geohash_dqcjrhx       -6.385425e-04 -0.000518
start_geohash_dqcjrhy       -3.858877e-04 -0.000094
start_geohash_dqcjrj7       -1.743836e-03 -0.000565
start_geohash_dqcjrjb       -5.209804e-04  0.000328
start_geohash_dqcjrjp       -4.031999e-04 -0.000416
start_geohash_dqcjrjw       -4.157110e-04  0.000303
start_geohash_dqcjrk4       -1.271378e-03  0.000034
start_geohash_dqcjrk6       -7.670284e-04 -0.000367
start_geohash_dqcjrkc       -8.114822e-04 -0.000334
start_geohash_dqcjrkw       -3.261063e-04  0.000566
start_geohash_dqcjrmm       -3.062316e-05  0.000431
start_geohash_dqcjrn4       -4.360254e-04 -0.000855
start_geohash_dqcjrn6       -4.177229e-04 -0.000205
start_geohash_dqcjrnd       -9.789955e-04  0.000801
start_geohash_dqcjrnn       -9.491471e-04 -0.001862
start_geohash_dqcjrny       -6.807397e-04 -0.000432
start_geohash_dqcjrp5       -1.759941e-04 -0.000447
start_geohash_dqcjrp6       -3.505790e-04 -0.000114
start_geohash_dqcjrpf       -7.083400e-04  0.000777
start_geohash_dqcjrpj       -5.846667e-04 -0.000116
start_geohash_dqcjrq4       -6.978080e-05 -0.000009
start_geohash_dqcjrq9       -6.190931e-04  0.000239
start_geohash_dqcjrr3       -1.194816e-04 -0.000447
start_geohash_dqcjrs3       -1.182366e-03 -0.001200
start_geohash_dqcjrsh       -2.039854e-04 -0.000592
start_geohash_dqcjrsp       -2.419018e-04  0.000224
start_geohash_dqcjrsy       -1.886550e-04 -0.000081
start_geohash_dqcjruc       -2.821231e-04 -0.000947
start_geohash_dqcjrvb        2.153306e-04  0.000264
start_geohash_dqcjrvm       -7.010644e-05  0.000379
start_geohash_dqcjrvt       -1.233555e-04  0.000594
start_geohash_dqcjrvx       -4.543749e-05 -0.000450
start_geohash_dqcjrwc       -6.377608e-05 -0.000437
start_geohash_dqcjrys       -1.218570e-04 -0.000443
start_geohash_dqcjrzm       -4.232365e-04 -0.000241
start_geohash_dqcjrzr       -1.142773e-04  0.000148
start_geohash_dqcjrzv        2.151125e-05  0.000080
start_geohash_dqcjt2h        3.792841e-05  0.000011
start_geohash_dqcjtb4       -1.773531e-04 -0.001361
start_geohash_dqcjtbd       -1.211754e-04  0.000615
start_geohash_dqcjtfx       -4.906236e-05 -0.000022
start_geohash_dqcjtmr        8.566523e-07 -0.000139
start_geohash_dqcjtu9        6.841160e-05 -0.000093
start_geohash_dqcjtug        7.477923e-05 -0.000005
start_geohash_dqcjtuh       -2.043965e-06  0.000200
start_geohash_dqcjtxt        1.325626e-04  0.000137
start_geohash_dqcjv7d        3.352485e-04  0.000105
start_geohash_dqcjv87        1.343533e-03  0.000105
start_geohash_dqcjv8n        2.227658e-05 -0.000096
start_geohash_dqcjv8r        1.300167e-04  0.000175
start_geohash_dqcjv8v        9.186056e-04  0.000564
start_geohash_dqcjv9f        2.946499e-04 -0.000137
start_geohash_dqcjv9s        2.088087e-04 -0.000082
start_geohash_dqcjvd1        1.532275e-04 -0.000246
start_geohash_dqcjvd3        9.479638e-04  0.000251
start_geohash_dqcjvee        1.314018e-04 -0.000129
start_geohash_dqcjw0e       -7.191469e-05  0.000444
start_geohash_dqcjw14        1.007421e-05 -0.000012
start_geohash_dqcjw16       -1.985892e-05  0.000487
start_geohash_dqcjw1b       -3.583910e-06 -0.000046
start_geohash_dqcjw2p       -2.285386e-04 -0.000288
start_geohash_dqcjw3e       -1.654710e-05 -0.000502
start_geohash_dqcjw3j       -5.428541e-05 -0.000037
start_geohash_dqcjw40       -7.838657e-05  0.000058
start_geohash_dqcjw5x        1.495808e-04  0.000873
start_geohash_dqcjw64       -3.454026e-05 -0.000282
start_geohash_dqcjwcn        1.050396e-04 -0.000262
start_geohash_dqcjwj7        2.991928e-05  0.000411
start_geohash_dqcjwm2        2.836645e-05  0.000243
start_geohash_dqcjx06       -3.377996e-05  0.000602
start_geohash_dqcjx0n        1.899799e-04  0.000355
start_geohash_dqcjx16        7.306319e-05 -0.000050
start_geohash_dqcjx20       -3.897340e-04 -0.000649
start_geohash_dqcjx2k       -1.295446e-04  0.000371
start_geohash_dqcjx30       -5.359563e-05 -0.000165
start_geohash_dqcjx3h       -4.563126e-05 -0.000238
start_geohash_dqcjx44        8.504543e-05  0.000336
start_geohash_dqcjx4w        5.498082e-05 -0.000077
start_geohash_dqcjx5f        1.032321e-04 -0.000242
start_geohash_dqcjx5r        3.475885e-05  0.000273
start_geohash_dqcjx5v        3.104740e-05  0.000191
start_geohash_dqcjx66        1.492303e-04  0.000121
start_geohash_dqcjx7g        6.980985e-05  0.000079
start_geohash_dqcjxbr       -2.309845e-04 -0.000129
start_geohash_dqcjxdb       -4.027363e-05 -0.000211
start_geohash_dqcjxg0        9.320545e-05 -0.001337
start_geohash_dqcjxgb       -3.522698e-05  0.000020
start_geohash_dqcjxhw        3.113568e-04 -0.000041
start_geohash_dqcjxjy        9.582251e-05 -0.000173
start_geohash_dqcjxn1        1.652488e-04  0.000117
start_geohash_dqcjxpq        3.272189e-04  0.000324
start_geohash_dqcjxqn        1.884178e-04  0.000174
start_geohash_dqcjxrn        1.305353e-03  0.000215
start_geohash_dqcjxs0        1.417014e-04 -0.000097
start_geohash_dqcjxut        4.011064e-05 -0.000246
start_geohash_dqcjxx4        2.133402e-04  0.000053
start_geohash_dqcjxxt        1.351975e-04 -0.000055
start_geohash_dqcjxzj        1.938445e-04  0.000126
start_geohash_dqcjyfy        1.523679e-04  0.000071
start_geohash_dqcjz1h        1.869789e-05  0.000005
start_geohash_dqcjz31        1.632493e-04  0.000023
start_geohash_dqcjz4s        1.388619e-04 -0.000096
start_geohash_dqcjz51        1.936096e-04  0.000130
start_geohash_dqcjz5p        2.862590e-04  0.000296
start_geohash_dqcjz6n        2.208044e-04 -0.000073
start_geohash_dqcjz8v        3.436546e-04 -0.000385
start_geohash_dqcjzc6        6.772365e-04  0.000523
start_geohash_dqckbp7        1.540982e-04  0.000195
start_geohash_dqckbqs        1.003093e-04  0.000162
start_geohash_dqckbrk        4.121073e-05  0.000024
start_geohash_dqcm01m        7.103337e-05  0.000031
start_geohash_dqcm04q        3.762562e-05  0.000268
start_geohash_dqcm05d       -2.038756e-05  0.000161
start_geohash_dqcm05m       -1.540153e-05 -0.000154
start_geohash_dqcm0d8        6.458140e-05  0.000124
start_geohash_dqcm0ez        1.134896e-04 -0.000196
start_geohash_dqcm0hp        2.164264e-05 -0.000128
start_geohash_dqcm0hw        7.757184e-05  0.000203
start_geohash_dqcm0ju       -1.576846e-04  0.000494
start_geohash_dqcm0kx        3.228386e-05 -0.000053
start_geohash_dqcm0mx        4.582464e-04  0.000738
start_geohash_dqcm0n3       -2.071447e-04  0.000140
start_geohash_dqcm0n7       -1.330126e-05 -0.000236
start_geohash_dqcm0nb        3.441668e-04 -0.000055
start_geohash_dqcm0ns        3.931534e-04 -0.000137
start_geohash_dqcm0pf       -4.395849e-04 -0.000366
start_geohash_dqcm0pn       -3.077699e-04  0.000250
start_geohash_dqcm0pw       -3.510094e-04 -0.000294
start_geohash_dqcm0q2       -4.647856e-05  0.000258
start_geohash_dqcm0qf       -1.561026e-04  0.000141
start_geohash_dqcm0re       -2.484681e-04  0.000127
start_geohash_dqcm0xy        2.745498e-05 -0.000041
start_geohash_dqcm1r8        6.981231e-05  0.000315
start_geohash_dqcm1w8        1.558219e-04 -0.000160
start_geohash_dqcm20e       -1.938104e-04 -0.000003
start_geohash_dqcm20q       -1.305344e-04 -0.000087
start_geohash_dqcm21d       -1.366017e-04  0.000548
start_geohash_dqcm21n       -4.458867e-04 -0.000340
start_geohash_dqcm21w       -2.354232e-04 -0.000293
start_geohash_dqcm22s       -8.065162e-05 -0.000202
start_geohash_dqcm231       -1.608145e-04 -0.000189
start_geohash_dqcm23c       -3.080371e-04 -0.001027
start_geohash_dqcm245       -4.134351e-05 -0.000107
start_geohash_dqcm24g       -1.517137e-04 -0.000008
start_geohash_dqcm24w       -1.080201e-04  0.000027
start_geohash_dqcm25s        1.720326e-05 -0.000155
start_geohash_dqcm27y        2.365645e-04  0.000723
start_geohash_dqcm28c        2.343347e-05  0.000067
start_geohash_dqcm290       -3.736766e-05  0.000101
start_geohash_dqcm2hq       -3.175573e-05  0.000518
start_geohash_dqcm2ju       -2.197734e-05  0.000047
start_geohash_dqcm2n8       -3.272044e-05 -0.000796
start_geohash_dqcm2p8       -6.428822e-05  0.000318
start_geohash_dqcm2r9       -7.727885e-05 -0.000066
start_geohash_dqcm2rn        1.184983e-04 -0.000160
start_geohash_dqcm2w5        4.276470e-06 -0.000042
start_geohash_dqcm318       -7.864146e-06  0.000046
start_geohash_dqcm375        1.231010e-04  0.000121
start_geohash_dqcm5z9        4.403056e-04  0.000312
start_geohash_dqcm737        4.321733e-04  0.000024
start_geohash_dqcm813        8.578056e-06 -0.000201
start_geohash_dqcm830        3.642437e-05 -0.000224
start_geohash_dqcm839        4.561588e-05 -0.000020
start_geohash_dqcm8be        8.694923e-05 -0.000029
start_geohash_dqcm8e9        4.448160e-04  0.000411
start_geohash_dqcm8er        2.569978e-04 -0.000407
start_geohash_dqcm8q8        1.780363e-04 -0.000019
start_geohash_dqcm8v7        2.765322e-04 -0.000208
start_geohash_dqcm8y5        1.658995e-04  0.000069
start_geohash_dqcm95t        1.528562e-04 -0.000232
start_geohash_dqcm9h3        1.495233e-04  0.000165
start_geohash_dqcm9kc        5.071821e-04  0.000457
start_geohash_dqcm9np        2.072013e-04 -0.000047
start_geohash_dqcmfeg        4.021840e-04  0.000168
start_geohash_dqcnhuw        1.065863e-03 -0.000245
start_geohash_dqcnj5x        3.568539e-04 -0.000212
start_geohash_dqcnk9e        4.937358e-04 -0.000018
start_geohash_dqcnke2        4.788696e-04 -0.000036
start_geohash_dqcnkjn        1.496524e-03 -0.000410
start_geohash_dqcnndr        2.004154e-04 -0.000139
start_geohash_dqcnndt        1.042013e-03 -0.000305
start_geohash_dqcnnsm        8.840180e-04 -0.000335
start_geohash_dqcns19        5.643528e-04  0.000132
start_geohash_dqcns48        6.884501e-04  0.000061
start_geohash_dqcq35j        4.640535e-04 -0.000229
start_capacity_bin_15–30 $  -5.198665e-02  0.010980
start_capacity_bin_30–60 $  -2.088911e-02 -0.007563
start_capacity_bin_7–10 $    4.112017e-04 -0.000407

n_clusters = 3 
agg_clustering = AgglomerativeClustering(n_clusters=n_clusters, metric='euclidean', linkage='ward')
cluster_labels = agg_clustering.fit_predict(X_processed)
algo_name = "AgglomerativeClustering"


grouped_sample['cluster_label'] = cluster_labels
grouped_sample['cluster_label'] = grouped_sample['cluster_label'].astype(str) 


if len(set(cluster_labels)) > 1: 
    sil_score = silhouette_score(X_processed, cluster_labels)
    print(f"{algo_name} - Silhouette Score = {sil_score:.4f}")
else:
    print(f"{algo_name} - عدد العناقيد غير كافٍ، لا يمكن حساب Silhouette Score.")



print(f"\n--- Cluster Description for {algo_name} ---")

print("\nAverage Values of Numerical Features per Cluster:")
numerical_summary = grouped_sample.groupby('cluster_label')[numeric_features].mean()
print(numerical_summary)

print("\nDistribution of Categorical Features per Cluster:")
for feature in categorical_features:
    print(f"\nFeature: {feature}")
    cluster_counts = grouped_sample.groupby(['cluster_label', feature]).size().unstack(fill_value=0)
    cluster_percentages = cluster_counts.apply(lambda x: x / x.sum(), axis=1) * 100
    print(cluster_percentages.round(2)) 
print("\nCluster Sizes:")
print(grouped_sample['cluster_label'].value_counts().sort_index())

pca = PCA(n_components=2)
components = pca.fit_transform(X_processed)

pca_df = pd.DataFrame(data = components, columns = ['PC1', 'PC2'])
pca_df['cluster_label'] = grouped_sample['cluster_label']

fig = px.scatter(
    pca_df,
    x='PC1',
    y='PC2',
    color='cluster_label',
    title=f'{algo_name} Clustering (PCA Reduced Dimensions)',
    labels={'PC1': 'Principal Component 1', 'PC2': 'Principal Component 2'}
)

fig.add_trace(go.Scatter(
    x=centroids_pca[:, 0], y=centroids_pca[:, 1],
    mode='markers',
    marker=dict(size=15, symbol='x', color='black'),
    name='Centroids'
))

#fig.show()
image_path = r"C:\Users\ASUS\OneDrive\Desktop\images\Screenshot 2025-06-20 185853.png"

image = Image.open(image_path)
display(image)

processed_feature_names = numeric_features[:]
preprocessor.fit(grouped_sample) 
ohe_feature_names = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features)
processed_feature_names.extend(ohe_feature_names)

loadings = pd.DataFrame(pca.components_.T, columns=['PC1', 'PC2'], index=processed_feature_names)
print("\nPCA Loadings (Contribution of Original Features to Principal Components):")
print(loadings.to_string())

AgglomerativeClustering - Silhouette Score = 0.0733

--- Cluster Description for AgglomerativeClustering ---

Average Values of Numerical Features per Cluster:
                    temp   humidity  windspeed  ride_duration_min  \
cluster_label                                                       
0              17.410642  64.383970  24.208004          16.279209   
1              15.403571  63.510714  21.771429          86.464286   
2               5.338426  66.964576  12.494463          11.096264   

               cost_category  distance_to_business_area  \
cluster_label                                             
0                   2.314330                   0.018310   
1                   2.535714                   0.292122   
2                   2.378919                   0.013400   

               start_distance_to_shuttle_m  start_distance_to_metro_m  
cluster_label                                                          
0                              1313.107671                 125.703616  
1                             28337.557629                2265.220885  
2                               815.563954                 104.854869  

Distribution of Categorical Features per Cluster:

Feature: rideable_type
rideable_type  classic_bike  electric_bike
cluster_label                             
0                     50.79          49.21
1                     64.29          35.71
2                     47.36          52.64

Feature: member_casual
member_casual  casual  member
cluster_label                
0               50.68   49.32
1               82.14   17.86
2               47.36   52.64

Feature: weather_bin
weather_bin    Cloudy  Rainy  Sunny
cluster_label                      
0               12.50  44.00  43.51
1               25.00  32.14  42.86
2               95.66   1.53   2.80

Feature: start_geohash
start_geohash  dqbvrzn  dqbvx3p  dqbvx7w  dqbvx96  dqbvxc8  dqbvxe9  dqbvxek  \
cluster_label                                                                  
0                 0.00     0.00     0.00     0.00     0.00     0.00     0.00   
1                 3.57     7.14     3.57     3.57     7.14     3.57    14.29   
2                 0.00     0.00     0.00     0.00     0.00     0.00     0.00   

start_geohash  dqbvxeu  dqbvxfe  dqbvxgd  ...  dqcnj5x  dqcnk9e  dqcnke2  \
cluster_label                             ...                              
0                 0.00     0.00     0.00  ...     0.02     0.02     0.02   
1                 3.57     7.14     3.57  ...     0.00     0.00     0.00   
2                 0.00     0.00     0.00  ...     0.00     0.00     0.00   

start_geohash  dqcnkjn  dqcnndr  dqcnndt  dqcnnsm  dqcns19  dqcns48  dqcq35j  
cluster_label                                                                 
0                 0.07     0.00     0.09     0.04     0.02     0.02     0.02  
1                 0.00     0.00     0.00     0.00     0.00     0.00     0.00  
2                 0.00     0.07     0.00     0.07     0.00     0.00     0.00  

[3 rows x 606 columns]

Feature: start_capacity_bin
start_capacity_bin  10–15 $  15–30 $  30–60 $  7–10 $
cluster_label                                        
0                     22.71    65.75    11.47    0.07
1                     82.14    17.86     0.00    0.00
2                     20.01    64.78    15.01    0.20

Cluster Sizes:
cluster_label
0    4473
1      28
2    1499
Name: count, dtype: int64

PCA Loadings (Contribution of Original Features to Principal Components):
                                      PC1       PC2
temp                         1.473775e-02  0.383722
humidity                    -2.515442e-02 -0.529548
windspeed                    5.267178e-03  0.625646
ride_duration_min            7.275576e-02  0.250793
cost_category                6.636345e-02 -0.264933
distance_to_business_area    6.096214e-01 -0.026776
start_distance_to_shuttle_m  6.227339e-01 -0.025858
start_distance_to_metro_m    4.736822e-01  0.020366
rideable_type_electric_bike -1.449674e-02  0.002489
member_casual_member        -4.160469e-02  0.060349
weather_bin_Rainy           -7.180000e-03 -0.009047
weather_bin_Sunny            1.317847e-02  0.204815
start_geohash_dqbvx3p        2.398170e-03 -0.000365
start_geohash_dqbvx7w        1.081112e-03  0.000072
start_geohash_dqbvx96        1.238706e-03  0.000015
start_geohash_dqbvxc8        2.134598e-03 -0.000409
start_geohash_dqbvxe9        1.194380e-03  0.000014
start_geohash_dqbvxek        4.865869e-03 -0.000163
start_geohash_dqbvxeu        1.221426e-03  0.000229
start_geohash_dqbvxfe        2.015529e-03  0.000190
start_geohash_dqbvxgd        1.049133e-03 -0.000209
start_geohash_dqbvxs5        1.231048e-03 -0.000009
start_geohash_dqbvxsm        2.461707e-03 -0.000091
start_geohash_dqbvxsw        2.396488e-03 -0.000162
start_geohash_dqbvxts        2.633608e-03 -0.000037
start_geohash_dqbvxv0        1.193487e-03 -0.000195
start_geohash_dqbvxzb        2.820167e-03  0.000116
start_geohash_dqchbuk        7.681811e-04 -0.000093
start_geohash_dqchbv1        7.979695e-04 -0.000110
start_geohash_dqchby0        6.562643e-04 -0.000093
start_geohash_dqchbyf        7.080968e-04 -0.000231
start_geohash_dqchtzv        6.710258e-04  0.000129
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start_geohash_dqcjqx8       -2.272340e-04 -0.000061
start_geohash_dqcjqyd       -3.458692e-04 -0.000188
start_geohash_dqcjqyr       -1.467377e-03 -0.000474
start_geohash_dqcjqyz       -6.181996e-04  0.000080
start_geohash_dqcjqzq       -9.093878e-04 -0.000052
start_geohash_dqcjqzu        3.890018e-05  0.000167
start_geohash_dqcjr04       -7.423522e-04 -0.000277
start_geohash_dqcjr0e       -4.444542e-04 -0.000569
start_geohash_dqcjr0f       -1.959663e-04  0.000040
start_geohash_dqcjr0r       -1.885087e-04  0.000092
start_geohash_dqcjr0z       -4.145895e-04  0.000221
start_geohash_dqcjr15       -6.254817e-04  0.000057
start_geohash_dqcjr16       -4.557842e-04  0.000960
start_geohash_dqcjr17       -2.320338e-04  0.000372
start_geohash_dqcjr19       -5.734998e-04 -0.000591
start_geohash_dqcjr1c       -5.207606e-04  0.000248
start_geohash_dqcjr1n       -9.473773e-04 -0.000212
start_geohash_dqcjr1p       -3.420158e-04 -0.000083
start_geohash_dqcjr1s       -3.765516e-04  0.000144
start_geohash_dqcjr1t       -5.924234e-04  0.000687
start_geohash_dqcjr1z       -4.381796e-04  0.000348
start_geohash_dqcjr22       -3.024926e-04  0.001308
start_geohash_dqcjr29       -3.297446e-04  0.000400
start_geohash_dqcjr2e       -2.261471e-04  0.000124
start_geohash_dqcjr2w       -1.817512e-04 -0.000045
start_geohash_dqcjr30       -2.316954e-04  0.000290
start_geohash_dqcjr33       -6.823286e-04 -0.000515
start_geohash_dqcjr34       -6.553971e-04  0.000019
start_geohash_dqcjr3h       -2.575163e-04  0.000579
start_geohash_dqcjr3r       -2.436740e-04 -0.000100
start_geohash_dqcjr3s       -4.976425e-04  0.000174
start_geohash_dqcjr40       -3.409280e-04  0.000274
start_geohash_dqcjr45       -7.973528e-04  0.001341
start_geohash_dqcjr49       -2.073745e-04  0.000614
start_geohash_dqcjr4d       -1.233599e-03 -0.000198
start_geohash_dqcjr4j       -3.917400e-04 -0.000532
start_geohash_dqcjr4w       -1.616738e-03 -0.001410
start_geohash_dqcjr53       -2.510312e-03 -0.001107
start_geohash_dqcjr54       -1.752365e-03  0.000411
start_geohash_dqcjr5e       -7.818403e-04 -0.000173
start_geohash_dqcjr5g       -1.876519e-03 -0.002704
start_geohash_dqcjr5h       -9.293022e-04  0.001141
start_geohash_dqcjr5n       -8.736780e-04 -0.001225
start_geohash_dqcjr64       -1.081437e-03 -0.000314
start_geohash_dqcjr6b       -7.405456e-04  0.000623
start_geohash_dqcjr6d       -1.352707e-03 -0.001315
start_geohash_dqcjr6h       -2.013586e-03 -0.000178
start_geohash_dqcjr6z       -7.837822e-04  0.000658
start_geohash_dqcjr71       -1.272294e-03  0.000941
start_geohash_dqcjr7c       -6.840517e-04 -0.000380
start_geohash_dqcjr7v       -5.470992e-04  0.000304
start_geohash_dqcjr80       -1.075503e-03 -0.000333
start_geohash_dqcjr8x       -1.200393e-03 -0.000565
start_geohash_dqcjr91       -1.075878e-03 -0.000147
start_geohash_dqcjr95       -7.041566e-04 -0.001543
start_geohash_dqcjr97       -5.374598e-04 -0.000039
start_geohash_dqcjr99       -6.452289e-04  0.000454
start_geohash_dqcjr9n       -2.586702e-03 -0.003495
start_geohash_dqcjr9r       -9.755865e-04 -0.000683
start_geohash_dqcjr9u       -3.007900e-04 -0.000779
start_geohash_dqcjr9y       -1.813834e-03 -0.000353
start_geohash_dqcjrb5       -3.825163e-04 -0.001013
start_geohash_dqcjrbj       -2.171983e-04 -0.000082
start_geohash_dqcjrbt       -4.113281e-04 -0.000011
start_geohash_dqcjrbz       -5.105964e-04  0.000410
start_geohash_dqcjrc8       -1.209915e-03 -0.001179
start_geohash_dqcjrce       -1.263998e-03  0.000193
start_geohash_dqcjrcn       -7.430203e-04  0.000737
start_geohash_dqcjrct       -7.983318e-04 -0.000028
start_geohash_dqcjrcv       -3.369988e-05  0.000121
start_geohash_dqcjrcx       -2.568467e-04 -0.000321
start_geohash_dqcjrd0       -4.931551e-04  0.000218
start_geohash_dqcjrd1       -2.846111e-04  0.000686
start_geohash_dqcjrdw       -1.505854e-04 -0.001060
start_geohash_dqcjre1        2.669464e-04 -0.000058
start_geohash_dqcjreb       -8.306416e-04  0.000359
start_geohash_dqcjreg       -5.815801e-04 -0.000007
start_geohash_dqcjrew       -4.587242e-04  0.001034
start_geohash_dqcjrf5       -4.795231e-04  0.000055
start_geohash_dqcjrf8       -6.579631e-04 -0.000321
start_geohash_dqcjrfb       -4.202024e-04  0.000270
start_geohash_dqcjrfc       -1.357766e-03 -0.000884
start_geohash_dqcjrff       -1.059754e-03  0.000027
start_geohash_dqcjrfq       -1.015515e-04 -0.000149
start_geohash_dqcjrfu        2.024783e-05 -0.000927
start_geohash_dqcjrg8       -4.055040e-04  0.000273
start_geohash_dqcjrhc       -6.004489e-04 -0.000180
start_geohash_dqcjrhg       -1.561892e-03 -0.001110
start_geohash_dqcjrhn       -7.051932e-04  0.000579
start_geohash_dqcjrhs       -8.603771e-04  0.000028
start_geohash_dqcjrht       -5.267803e-04 -0.000363
start_geohash_dqcjrhx       -6.385425e-04 -0.000518
start_geohash_dqcjrhy       -3.858877e-04 -0.000094
start_geohash_dqcjrj7       -1.743836e-03 -0.000565
start_geohash_dqcjrjb       -5.209804e-04  0.000328
start_geohash_dqcjrjp       -4.031999e-04 -0.000416
start_geohash_dqcjrjw       -4.157110e-04  0.000303
start_geohash_dqcjrk4       -1.271378e-03  0.000034
start_geohash_dqcjrk6       -7.670284e-04 -0.000367
start_geohash_dqcjrkc       -8.114822e-04 -0.000334
start_geohash_dqcjrkw       -3.261063e-04  0.000566
start_geohash_dqcjrmm       -3.062316e-05  0.000431
start_geohash_dqcjrn4       -4.360254e-04 -0.000855
start_geohash_dqcjrn6       -4.177229e-04 -0.000205
start_geohash_dqcjrnd       -9.789955e-04  0.000801
start_geohash_dqcjrnn       -9.491471e-04 -0.001862
start_geohash_dqcjrny       -6.807397e-04 -0.000432
start_geohash_dqcjrp5       -1.759941e-04 -0.000447
start_geohash_dqcjrp6       -3.505790e-04 -0.000114
start_geohash_dqcjrpf       -7.083400e-04  0.000777
start_geohash_dqcjrpj       -5.846667e-04 -0.000116
start_geohash_dqcjrq4       -6.978080e-05 -0.000009
start_geohash_dqcjrq9       -6.190931e-04  0.000239
start_geohash_dqcjrr3       -1.194816e-04 -0.000447
start_geohash_dqcjrs3       -1.182366e-03 -0.001200
start_geohash_dqcjrsh       -2.039854e-04 -0.000592
start_geohash_dqcjrsp       -2.419018e-04  0.000224
start_geohash_dqcjrsy       -1.886550e-04 -0.000081
start_geohash_dqcjruc       -2.821231e-04 -0.000947
start_geohash_dqcjrvb        2.153306e-04  0.000264
start_geohash_dqcjrvm       -7.010644e-05  0.000379
start_geohash_dqcjrvt       -1.233555e-04  0.000594
start_geohash_dqcjrvx       -4.543749e-05 -0.000450
start_geohash_dqcjrwc       -6.377608e-05 -0.000437
start_geohash_dqcjrys       -1.218570e-04 -0.000443
start_geohash_dqcjrzm       -4.232365e-04 -0.000241
start_geohash_dqcjrzr       -1.142773e-04  0.000148
start_geohash_dqcjrzv        2.151125e-05  0.000080
start_geohash_dqcjt2h        3.792841e-05  0.000011
start_geohash_dqcjtb4       -1.773531e-04 -0.001361
start_geohash_dqcjtbd       -1.211754e-04  0.000615
start_geohash_dqcjtfx       -4.906236e-05 -0.000022
start_geohash_dqcjtmr        8.566523e-07 -0.000139
start_geohash_dqcjtu9        6.841160e-05 -0.000093
start_geohash_dqcjtug        7.477923e-05 -0.000005
start_geohash_dqcjtuh       -2.043965e-06  0.000200
start_geohash_dqcjtxt        1.325626e-04  0.000137
start_geohash_dqcjv7d        3.352485e-04  0.000105
start_geohash_dqcjv87        1.343533e-03  0.000105
start_geohash_dqcjv8n        2.227658e-05 -0.000096
start_geohash_dqcjv8r        1.300167e-04  0.000175
start_geohash_dqcjv8v        9.186056e-04  0.000564
start_geohash_dqcjv9f        2.946499e-04 -0.000137
start_geohash_dqcjv9s        2.088087e-04 -0.000082
start_geohash_dqcjvd1        1.532275e-04 -0.000246
start_geohash_dqcjvd3        9.479638e-04  0.000251
start_geohash_dqcjvee        1.314018e-04 -0.000129
start_geohash_dqcjw0e       -7.191469e-05  0.000444
start_geohash_dqcjw14        1.007421e-05 -0.000012
start_geohash_dqcjw16       -1.985892e-05  0.000487
start_geohash_dqcjw1b       -3.583910e-06 -0.000046
start_geohash_dqcjw2p       -2.285386e-04 -0.000288
start_geohash_dqcjw3e       -1.654710e-05 -0.000502
start_geohash_dqcjw3j       -5.428541e-05 -0.000037
start_geohash_dqcjw40       -7.838657e-05  0.000058
start_geohash_dqcjw5x        1.495808e-04  0.000873
start_geohash_dqcjw64       -3.454026e-05 -0.000282
start_geohash_dqcjwcn        1.050396e-04 -0.000262
start_geohash_dqcjwj7        2.991928e-05  0.000411
start_geohash_dqcjwm2        2.836645e-05  0.000243
start_geohash_dqcjx06       -3.377996e-05  0.000602
start_geohash_dqcjx0n        1.899799e-04  0.000355
start_geohash_dqcjx16        7.306319e-05 -0.000050
start_geohash_dqcjx20       -3.897340e-04 -0.000649
start_geohash_dqcjx2k       -1.295446e-04  0.000371
start_geohash_dqcjx30       -5.359563e-05 -0.000165
start_geohash_dqcjx3h       -4.563126e-05 -0.000238
start_geohash_dqcjx44        8.504543e-05  0.000336
start_geohash_dqcjx4w        5.498082e-05 -0.000077
start_geohash_dqcjx5f        1.032321e-04 -0.000242
start_geohash_dqcjx5r        3.475885e-05  0.000273
start_geohash_dqcjx5v        3.104740e-05  0.000191
start_geohash_dqcjx66        1.492303e-04  0.000121
start_geohash_dqcjx7g        6.980985e-05  0.000079
start_geohash_dqcjxbr       -2.309845e-04 -0.000129
start_geohash_dqcjxdb       -4.027363e-05 -0.000211
start_geohash_dqcjxg0        9.320545e-05 -0.001337
start_geohash_dqcjxgb       -3.522698e-05  0.000020
start_geohash_dqcjxhw        3.113568e-04 -0.000041
start_geohash_dqcjxjy        9.582251e-05 -0.000173
start_geohash_dqcjxn1        1.652488e-04  0.000117
start_geohash_dqcjxpq        3.272189e-04  0.000324
start_geohash_dqcjxqn        1.884178e-04  0.000174
start_geohash_dqcjxrn        1.305353e-03  0.000215
start_geohash_dqcjxs0        1.417014e-04 -0.000097
start_geohash_dqcjxut        4.011064e-05 -0.000246
start_geohash_dqcjxx4        2.133402e-04  0.000053
start_geohash_dqcjxxt        1.351975e-04 -0.000055
start_geohash_dqcjxzj        1.938445e-04  0.000126
start_geohash_dqcjyfy        1.523679e-04  0.000071
start_geohash_dqcjz1h        1.869789e-05  0.000005
start_geohash_dqcjz31        1.632493e-04  0.000023
start_geohash_dqcjz4s        1.388619e-04 -0.000096
start_geohash_dqcjz51        1.936096e-04  0.000130
start_geohash_dqcjz5p        2.862590e-04  0.000296
start_geohash_dqcjz6n        2.208044e-04 -0.000073
start_geohash_dqcjz8v        3.436546e-04 -0.000385
start_geohash_dqcjzc6        6.772365e-04  0.000523
start_geohash_dqckbp7        1.540982e-04  0.000195
start_geohash_dqckbqs        1.003093e-04  0.000162
start_geohash_dqckbrk        4.121073e-05  0.000024
start_geohash_dqcm01m        7.103337e-05  0.000031
start_geohash_dqcm04q        3.762562e-05  0.000268
start_geohash_dqcm05d       -2.038756e-05  0.000161
start_geohash_dqcm05m       -1.540153e-05 -0.000154
start_geohash_dqcm0d8        6.458140e-05  0.000124
start_geohash_dqcm0ez        1.134896e-04 -0.000196
start_geohash_dqcm0hp        2.164264e-05 -0.000128
start_geohash_dqcm0hw        7.757184e-05  0.000203
start_geohash_dqcm0ju       -1.576846e-04  0.000494
start_geohash_dqcm0kx        3.228386e-05 -0.000053
start_geohash_dqcm0mx        4.582464e-04  0.000738
start_geohash_dqcm0n3       -2.071447e-04  0.000140
start_geohash_dqcm0n7       -1.330126e-05 -0.000236
start_geohash_dqcm0nb        3.441668e-04 -0.000055
start_geohash_dqcm0ns        3.931534e-04 -0.000137
start_geohash_dqcm0pf       -4.395849e-04 -0.000366
start_geohash_dqcm0pn       -3.077699e-04  0.000250
start_geohash_dqcm0pw       -3.510094e-04 -0.000294
start_geohash_dqcm0q2       -4.647856e-05  0.000258
start_geohash_dqcm0qf       -1.561026e-04  0.000141
start_geohash_dqcm0re       -2.484681e-04  0.000127
start_geohash_dqcm0xy        2.745498e-05 -0.000041
start_geohash_dqcm1r8        6.981231e-05  0.000315
start_geohash_dqcm1w8        1.558219e-04 -0.000160
start_geohash_dqcm20e       -1.938104e-04 -0.000003
start_geohash_dqcm20q       -1.305344e-04 -0.000087
start_geohash_dqcm21d       -1.366017e-04  0.000548
start_geohash_dqcm21n       -4.458867e-04 -0.000340
start_geohash_dqcm21w       -2.354232e-04 -0.000293
start_geohash_dqcm22s       -8.065162e-05 -0.000202
start_geohash_dqcm231       -1.608145e-04 -0.000189
start_geohash_dqcm23c       -3.080371e-04 -0.001027
start_geohash_dqcm245       -4.134351e-05 -0.000107
start_geohash_dqcm24g       -1.517137e-04 -0.000008
start_geohash_dqcm24w       -1.080201e-04  0.000027
start_geohash_dqcm25s        1.720326e-05 -0.000155
start_geohash_dqcm27y        2.365645e-04  0.000723
start_geohash_dqcm28c        2.343347e-05  0.000067
start_geohash_dqcm290       -3.736766e-05  0.000101
start_geohash_dqcm2hq       -3.175573e-05  0.000518
start_geohash_dqcm2ju       -2.197734e-05  0.000047
start_geohash_dqcm2n8       -3.272044e-05 -0.000796
start_geohash_dqcm2p8       -6.428822e-05  0.000318
start_geohash_dqcm2r9       -7.727885e-05 -0.000066
start_geohash_dqcm2rn        1.184983e-04 -0.000160
start_geohash_dqcm2w5        4.276470e-06 -0.000042
start_geohash_dqcm318       -7.864146e-06  0.000046
start_geohash_dqcm375        1.231010e-04  0.000121
start_geohash_dqcm5z9        4.403056e-04  0.000312
start_geohash_dqcm737        4.321733e-04  0.000024
start_geohash_dqcm813        8.578056e-06 -0.000201
start_geohash_dqcm830        3.642437e-05 -0.000224
start_geohash_dqcm839        4.561588e-05 -0.000020
start_geohash_dqcm8be        8.694923e-05 -0.000029
start_geohash_dqcm8e9        4.448160e-04  0.000411
start_geohash_dqcm8er        2.569978e-04 -0.000407
start_geohash_dqcm8q8        1.780363e-04 -0.000019
start_geohash_dqcm8v7        2.765322e-04 -0.000208
start_geohash_dqcm8y5        1.658995e-04  0.000069
start_geohash_dqcm95t        1.528562e-04 -0.000232
start_geohash_dqcm9h3        1.495233e-04  0.000165
start_geohash_dqcm9kc        5.071821e-04  0.000457
start_geohash_dqcm9np        2.072013e-04 -0.000047
start_geohash_dqcmfeg        4.021840e-04  0.000168
start_geohash_dqcnhuw        1.065863e-03 -0.000245
start_geohash_dqcnj5x        3.568539e-04 -0.000212
start_geohash_dqcnk9e        4.937358e-04 -0.000018
start_geohash_dqcnke2        4.788696e-04 -0.000036
start_geohash_dqcnkjn        1.496524e-03 -0.000410
start_geohash_dqcnndr        2.004154e-04 -0.000139
start_geohash_dqcnndt        1.042013e-03 -0.000305
start_geohash_dqcnnsm        8.840180e-04 -0.000335
start_geohash_dqcns19        5.643528e-04  0.000132
start_geohash_dqcns48        6.884501e-04  0.000061
start_geohash_dqcq35j        4.640535e-04 -0.000229
start_capacity_bin_15–30 $  -5.198665e-02  0.010980
start_capacity_bin_30–60 $  -2.088911e-02 -0.007563
start_capacity_bin_7–10 $    4.112017e-04 -0.000407

def haversine_distance(lat1, lon1, lat2, lon2):
    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
    dlat = lat2 - lat1
    dlon = lon2 - lon1
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a))
    r = 6371  
    return c * r

df['distance_km'] = df.apply(
    lambda row: haversine_distance(row['start_lat_y'], row['start_lng_y'], row['end_lat_y'], row['end_lng_y']),
    axis=1
)

if 'ride_duration_min' not in df.columns:
    df['started_at'] = pd.to_datetime(df['started_at'])
    df['ended_at'] = pd.to_datetime(df['ended_at'])
    df['ride_duration_min'] = (df['ended_at'] - df['started_at']).dt.total_seconds() / 60

df['min_per_km'] = df['ride_duration_min'] / df['distance_km']

df = df.replace([np.inf, -np.inf], np.nan)
df['min_per_km'].value_counts()

min_per_km
4.345489     432
4.116779     426
4.574199     402
4.231134     376
4.802909     375
            ... 
16.338615      1
6.870589       1
91.682762      1
14.062801      1
6.114291       1
Name: count, Length: 2108775, dtype: int64

df_clean = df[(df['min_per_km'] >= 1) & (df['min_per_km'] <= 20)]
classic_bike_business = df_clean[
    (df_clean['rideable_type'] == 'classic_bike') &
    (df_clean['business_area_fee'] > 0)
]


electric_bike_business = df_clean[
    (df_clean['rideable_type'] == 'electric_bike') &
    (df_clean['business_area_fee'] > 0)
]


print("Classic Bike in business area:\n", classic_bike_business['min_per_km'].describe())
print("\nElectric Bike in business area:\n", electric_bike_business['min_per_km'].describe())

Classic Bike in business area:
 count    872661.000000
mean          7.638109
std           3.134692
min           1.988944
25%           5.499772
50%           6.743477
75%           8.764316
max          19.999630
Name: min_per_km, dtype: float64

Electric Bike in business area:
 count    453255.000000
mean          5.594365
std           2.575858
min           2.011350
25%           4.000943
50%           4.882934
75%           6.243582
max          19.999209
Name: min_per_km, dtype: float64

classic_bike_business = df_clean[
    (df_clean['rideable_type'] == 'classic_bike') &
    (df_clean['business_area_fee'] == 0)
]


electric_bike_business = df_clean[
    (df_clean['rideable_type'] == 'electric_bike') &
    (df_clean['business_area_fee'] == 0)
]


print("Classic Bike not in business area:\n", classic_bike_business['min_per_km'].describe())
print("\nElectric Bike not in business area:\n", electric_bike_business['min_per_km'].describe())

Classic Bike not in business area:
 count    1.532502e+06
mean     6.951044e+00
std      2.695855e+00
min      1.551741e+00
25%      5.193204e+00
50%      6.260399e+00
75%      7.840503e+00
max      1.999947e+01
Name: min_per_km, dtype: float64

Electric Bike not in business area:
 count    1.141262e+06
mean     5.252941e+00
std      2.230270e+00
min      1.140689e+00
25%      3.883273e+00
50%      4.702220e+00
75%      5.882012e+00
max      1.999955e+01
Name: min_per_km, dtype: float64

df_trips = df.merge(df_station[['name', 'inside_dc']], left_on='start_station_name', right_on='name', how='left')
df_trips.rename(columns={'inside_dc': 'start_inside_dc'}, inplace=True)
df_trips.drop('name', axis=1, inplace=True)

df_trips = df_trips.merge(df_station[['name', 'inside_dc']], left_on='end_station_name', right_on='name', how='left')
df_trips.rename(columns={'inside_dc': 'end_inside_dc'}, inplace=True)
df_trips.drop('name', axis=1, inplace=True)

outside_dc_trips = df_trips[(df_trips['start_inside_dc'] == False) | (df_trips['end_inside_dc'] == False)]
count_outside_dc = len(outside_dc_trips)
print(f"عدد الرحلات التي بدايتها أو نهايتها خارج DC: {count_outside_dc}")

عدد الرحلات التي بدايتها أو نهايتها خارج DC: 738594

cross_boundary_trips = df_trips[
    (
        ((df_trips['start_inside_dc'] == False) & (df_trips['end_inside_dc'] == True)) |
        ((df_trips['start_inside_dc'] == True) & (df_trips['end_inside_dc'] == False))
    )
    &
    (df_trips['trip_in_business_area'] == False)
]

print(f"عدد الرحلات التي عبرت الحدود بين داخل وخارج DC ولم تنتهِ في منطقة تجارية: {len(cross_boundary_trips):,}")

عدد الرحلات التي عبرت الحدود بين داخل وخارج DC ولم تنتهِ في منطقة تجارية: 136,945

bike_type_counts = cross_boundary_trips['rideable_type'].value_counts()

print("توزيع نوع الدراجة للرحلات خارج DC:")
print(bike_type_counts)

توزيع نوع الدراجة للرحلات خارج DC:
rideable_type
classic_bike     77991
electric_bike    58954
Name: count, dtype: Int64

long_valid_rides = df_trips[(df_trips['distance_km'] >10)]

ride_type_counts = long_valid_rides['rideable_type'].value_counts()
ride_type_percent = ride_type_counts / ride_type_counts.sum() * 100


print("\ ازا مسافة 10 كيلومترnالنسبة المئوية لكل نوع:")
print(ride_type_percent)

\ ازا مسافة 10 كيلومترnالنسبة المئوية لكل نوع:
rideable_type
electric_bike    51.440212
classic_bike     48.559788
Name: count, dtype: Float64

long_valid_rides = df_trips[(df_trips['distance_km'] <1.5)]
ride_type_counts = long_valid_rides['rideable_type'].value_counts()
ride_type_percent = ride_type_counts / ride_type_counts.sum() * 100


print("\ ازا مسافة 2 كيلومترnالنسبة المئوية لكل نوع:")
print(ride_type_percent)

\ ازا مسافة 2 كيلومترnالنسبة المئوية لكل نوع:
rideable_type
classic_bike     68.607614
electric_bike    31.392386
Name: count, dtype: Float64

closest_stations = []

for idx, row in df_station.iterrows():
    station_id = row['id']
    station_name = row['name']
    coord1 = (row['lat'], row['lng'])

    df_others = df_station[df_station['id'] != station_id].copy()
    df_others['distance_km'] = df_others.apply(
        lambda x: geodesic(coord1, (x['lat'], x['lng'])).km, axis=1)


    nearest = df_others.loc[df_others['distance_km'].idxmin()]
    closest_stations.append({
        'station_id': station_id,
        'station_name': station_name,
        'closest_station_id': nearest['id'],
        'closest_station_name': nearest['name'],
        'distance_km': nearest['distance_km']
    })

df_closest = pd.DataFrame(closest_stations)

print(df_closest['distance_km'].describe())

count    819.000000
mean       0.396832
std        0.291111
min        0.001051
25%        0.213340
50%        0.316546
75%        0.490054
max        3.399025
Name: distance_km, dtype: float64

df_closest_under_600m = df_closest[df_closest['distance_km'] < 0.6]

df_links = df_trips[['start_station_name', 'end_station_name', 'ride_duration_min']]
df_merge = pd.merge(df_closest_under_600m, df_links,
                    left_on=['station_name', 'closest_station_name'],
                    right_on=['start_station_name', 'end_station_name'],
                    how='left')

df_with_trips = df_merge.dropna(subset=['ride_duration_min'])
duration_summary = df_with_trips.groupby(
    ['station_name', 'closest_station_name']
)['ride_duration_min'].agg(['count', 'mean', 'median', 'std']).reset_index()

print(duration_summary.head())

          station_name       closest_station_name  count       mean  median  \
0       10th & e st nw             11th & f st nw     87   7.926437     2.2   
1       10th & g st nw             11th & f st nw     29  35.986207    14.8   
2       10th & h st ne              8th & h st ne    136   7.883824     1.4   
3       10th & k st nw  12th st & new york ave nw     71  16.287324     3.8   
4  10th & monroe st ne         7th & monroe st ne    144  11.078472     2.0   

         std  
0  15.379687  
1  48.075757  
2  20.452149  
3  27.329706  
4  24.098403

len(df_with_trips)
#69740
len(df)
#6103742

6103742

us_holidays = [
    datetime.date(2024, 1, 1),   # New Year's Day
    datetime.date(2024, 1, 15),  # Martin Luther King Jr. Day
    datetime.date(2024, 2, 19),  # Presidents' Day
    datetime.date(2024, 5, 27),  # Memorial Day
    datetime.date(2024, 6, 19),  # Juneteenth
    datetime.date(2024, 7, 4),   # Independence Day
    datetime.date(2024, 9, 2),   # Labor Day
    datetime.date(2024, 10, 14), # Columbus Day
    datetime.date(2024, 11, 11), # Veterans Day
    datetime.date(2024, 11, 28), # Thanksgiving Day
    datetime.date(2024, 12, 25), # Christmas Day
]

df['is_holiday'] = df['start_date'].isin(us_holidays)
holiday_usage = df['is_holiday'].value_counts()
print("عدد الرحلات في أيام العطل مقابل الأيام العادية")
print(holiday_usage)

عدد الرحلات في أيام العطل مقابل الأيام العادية
is_holiday
False    5959025
True      144717
Name: count, dtype: int64

avg_duration_by_holiday = df.groupby('is_holiday')['ride_duration_min'].mean()
print("\nمتوسط مدة الرحلة في أيام العطل مقابل الأيام العادية")
print(avg_duration_by_holiday)

متوسط مدة الرحلة في أيام العطل مقابل الأيام العادية
is_holiday
False    16.336733
True     19.578255
Name: ride_duration_min, dtype: float64

holiday_users = df[df['is_holiday'] == True]['member_casual'].value_counts(normalize=True)
print("\nنسبة العضو والزائر في أيام العطل")
print(holiday_users)

نسبة العضو والزائر في أيام العطل
member_casual
member    0.609963
casual    0.390037
Name: proportion, dtype: Float64

locations_df['STATION_ID'] = locations_df['STATION_ID'].astype(str).str.strip()
locations_df['is_active'] = locations_df['STATION_STATUS'].str.lower().eq('active')
inactive_station_ids = locations_df[~locations_df['is_active']]['STATION_ID'].unique()

print("عدد المحطات غير الفعالة:", len(inactive_station_ids))
print(inactive_station_ids)

عدد المحطات غير الفعالة: 4
['08249e81-1f3f-11e7-bf6b-3863bb334450'
 '0824eb1b-1f3f-11e7-bf6b-3863bb334450'
 '0826547e-1f3f-11e7-bf6b-3863bb334450'
 'b8b6903d-fabc-48aa-af9b-2c5fb29344b9']

df['start_station_name_clean'] = df['start_station_name'].astype(str).str.strip().str.lower()
locations_df['NAME_clean'] = locations_df['NAME'].astype(str).str.strip().str.lower()

usage_vs_status = (
    df.merge(
        locations_df[['NAME_clean', 'is_active']],
        left_on='start_station_name_clean',
        right_on='NAME_clean',
        how='left'
    )
    .groupby('is_active')['ride_id']
    .count()
)

print("\n rides based on station status: ")
print(usage_vs_status)

 rides based on station status: 
is_active
False      30684
True     4771034
Name: ride_id, dtype: int64

df['start_station_name_clean'] = df['start_station_name'].astype(str).str.strip().str.lower()
locations_df['NAME_clean'] = locations_df['NAME'].astype(str).str.strip().str.lower()
inactive_names = locations_df[~locations_df['is_active']]['NAME_clean'].unique()
inactive_trips = df[df['start_station_name_clean'].isin(inactive_names)]
inactive_trips['started_at'] = pd.to_datetime(inactive_trips['started_at'])
monthly_counts = inactive_trips.resample('M', on='started_at')['ride_id'].count()

print("number of rides that are from in active sations ")
print(monthly_counts)

number of rides that are from in active sations 
started_at
2024-01-31    1148
2024-02-29    1523
2024-03-31    1960
2024-04-30    2420
2024-05-31    2392
2024-06-30    2951
2024-07-31    2987
2024-08-31    2923
2024-09-30    3314
2024-10-31    4536
2024-11-30    3143
2024-12-31    1387
Freq: M, Name: ride_id, dtype: int64

C:\Users\asus\AppData\Local\Temp\ipykernel_12476\988411189.py:5: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

	NAME_left	LATITUDE	LONGITUDE	STATION_STATUS
1	W&OD Trail/Sunset Hills Rd & Isaac Newton Sq	38.951419	-77.340281	active
5	Roosevelt Center & Crescent Rd	39.000310	-76.878005	active
6	Braddock Rd Metro North	38.814577	-77.052808	active
8	S Randolph St & Campbell Ave	38.840654	-77.088659	active
9	Crescent Rd & Ridge Rd	39.006191	-76.891247	active
...	...	...	...	...
787	King St & Peyton St	38.806002	-77.055911	active
789	Oxon Hill Park & Ride	38.798749	-77.001030	active
790	Fleet St & Ritchie Pkwy	39.076331	-77.141378	active
792	Briggs Chaney Park & Ride	39.077875	-76.941867	active
793	Old Meadow Rd & Cathy Ln	38.916703	-77.214448	active

	start_date	baseline_total_cost
0	2025-01-01	46992.011111
1	2025-01-02	48900.342222
2	2025-01-03	50808.673333
3	2025-01-04	52717.004444
4	2025-01-05	54625.335556
5	2025-01-06	56533.666667
6	2025-01-07	58441.997778
7	2025-01-08	60350.328889
8	2025-01-09	62258.660000
9	2025-01-10	64166.991111

	ds	trend	yhat_lower	yhat_upper	trend_lower	trend_upper	multiplicative_terms	multiplicative_terms_lower	multiplicative_terms_upper	weekly	weekly_lower	weekly_upper	additive_terms	additive_terms_lower	additive_terms_upper	yhat
0	2024-01-01	2343.491624	13747.085255	37305.805023	2343.491624	2343.491624	10.091589	10.091589	10.091589	10.091589	10.091589	10.091589	0.0	0.0	0.0	25993.046582
1	2024-01-02	2381.146574	17412.118634	41091.531163	2381.146574	2381.146574	11.004463	11.004463	11.004463	11.004463	11.004463	11.004463	0.0	0.0	0.0	28584.386158
2	2024-01-03	2418.801523	16319.582614	40903.772811	2418.801523	2418.801523	10.927195	10.927195	10.927195	10.927195	10.927195	10.927195	0.0	0.0	0.0	28849.516759
3	2024-01-04	2456.456472	18439.992842	41449.626941	2456.456472	2456.456472	11.160155	11.160155	11.160155	11.160155	11.160155	11.160155	0.0	0.0	0.0	29870.892428
4	2024-01-05	2494.183593	18256.230975	41575.668321	2494.183593	2494.183593	10.952976	10.952976	10.952976	10.952976	10.952976	10.952976	0.0	0.0	0.0	29812.915671
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
361	2024-12-27	3445.907616	28950.375190	54136.999177	3151.000802	3741.103629	10.952976	10.952976	10.952976	10.952976	10.952976	10.952976	0.0	0.0	0.0	41188.849721
362	2024-12-28	3384.846925	28599.053304	54902.341223	2983.054230	3772.332049	11.390827	11.390827	11.390827	11.390827	11.390827	11.390827	0.0	0.0	0.0	41941.052968
363	2024-12-29	3323.786234	24862.432060	51321.591794	2820.367000	3803.296743	10.436095	10.436095	10.436095	10.436095	10.436095	10.436095	0.0	0.0	0.0	38011.134463
364	2024-12-30	3262.725543	22944.953777	49364.204045	2639.014316	3835.014011	10.091589	10.091589	10.091589	10.091589	10.091589	10.091589	0.0	0.0	0.0	36188.811664
365	2024-12-31	3201.664853	23346.908402	52436.961865	2447.545392	3890.554549	11.004463	11.004463	11.004463	11.004463	11.004463	11.004463	0.0	0.0	0.0	38434.267555

small¶

medium¶

large¶

Sunny¶

Rainy¶

Cloudy¶

شرح المخطط¶

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شرح المخطط¶

بسبب عدد الرحلات الكبير المخطط لم يكن واضح لاستنتاج منه أي معلومة: وتم تجربة اخذ عينة تم ظهور قيم متطرفة بشكل أوضح لكن لايزال سيئ¶

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شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

شرح المخطط¶

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Sunny¶

Rainy¶

Cloudy¶

	NAME	STATION_TYPE	STATION_ID	STATION_STATUS	LAST_REPORTED	NUM_DOCKS_AVAILABLE	NUM_DOCKS_DISABLED	NUM_BIKES_AVAILABLE	NUM_EBIKES_AVAILABLE	NUM_BIKES_DISABLED	...	IS_RENTING	HAS_KIOSK	ELECTRIC_BIKE_SURCHARGE_WAIVER	EIGHTD_HAS_KEY_DISPENSER	CAPACITY	RENTAL_METHODS	REGION_ID	REGION_NAME	LATITUDE	LONGITUDE
0	Lincoln Memorial	classic	08254284-1f3f-11e7-bf6b-3863bb334450	NaN	2025/05/05 20:25:51+00	17	0	8	0	0	...	YES	YES	NO	NO	25	KEY,CREDITCARD	42.0	DCA-CABI	38.888255	-77.049437
1	W&OD Trail/Sunset Hills Rd & Isaac Newton Sq	classic	08263fbd-1f3f-11e7-bf6b-3863bb334450	NaN	2025/05/05 20:25:51+00	15	0	3	0	1	...	YES	YES	NO	NO	19	KEY,CREDITCARD	104.0	DCA-CABI	38.951419	-77.340281
2	17th St & Independence Ave SW	classic	082623bf-1f3f-11e7-bf6b-3863bb334450	NaN	2025/05/05 20:25:51+00	10	0	6	4	1	...	YES	YES	NO	NO	19	KEY,CREDITCARD	42.0	DCA-CABI	38.888097	-77.038325
3	8th & D St NW	classic	08256ac9-1f3f-11e7-bf6b-3863bb334450	NaN	2025/05/05 20:25:49+00	0	0	24	0	0	...	YES	NO	NO	NO	24	KEY,CREDITCARD	42.0	DCA-CABI	38.894851	-77.023240
4	Anacostia Ave & Benning Rd NE / River Terrace	classic	082518eb-1f3f-11e7-bf6b-3863bb334450	NaN	2025/05/05 20:25:49+00	4	0	10	0	1	...	YES	YES	NO	NO	15	KEY,CREDITCARD	42.0	DCA-CABI	38.896544	-76.960120
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
789	Oxon Hill Park & Ride	classic	ccd82461-11b7-4553-b70b-6cb7e5e4f8c2	NaN	2025/05/05 20:25:58+00	12	0	3	0	0	...	YES	YES	NO	NO	15	KEY,CREDITCARD	133.0	DCA-CABI	38.798749	-77.001030
790	Fleet St & Ritchie Pkwy	classic	0825b42a-1f3f-11e7-bf6b-3863bb334450	NaN	2025/05/05 20:25:58+00	10	0	4	1	1	...	YES	YES	NO	NO	15	KEY,CREDITCARD	43.0	DCA-CABI	39.076331	-77.141378
791	Pennsylvania & Minnesota Ave SE	classic	082498ac-1f3f-11e7-bf6b-3863bb334450	NaN	2025/05/05 20:26:00+00	4	0	7	0	0	...	YES	YES	NO	NO	11	KEY,CREDITCARD	42.0	DCA-CABI	38.873057	-76.971015
792	Briggs Chaney Park & Ride	classic	ac598bfa-89c4-4229-a397-a6279b4cd536	NaN	2025/05/05 20:26:00+00	14	0	9	0	0	...	YES	YES	NO	NO	23	KEY,CREDITCARD	44.0	DCA-CABI	39.077875	-76.941867
793	Old Meadow Rd & Cathy Ln	classic	1890899920583743818	NaN	2025/05/05 20:26:01+00	12	0	0	0	0	...	YES	YES	NO	NO	12	KEY,CREDITCARD	NaN	DCA-CABI	38.916703	-77.214448

	NAME	HAS_KIOSK	NUM_BIKES_AVAILABLE	CAPACITY
261	21st & L St NW	NO	14	16
702	Monroe Ave & Leslie Ave	NO	16	16
481	Rolfe St & 9th St S	NO	3	12
453	Eastern Market Metro	NO	2	32
615	20th & M St NW	NO	9	24
...	...	...	...	...
278	Rhode Island Ave Metro	YES	2	19
279	Emma Lee St & Morris St	YES	1	12
280	10th St & L'Enfant Plaza SW	YES	13	23
307	4th & Florida Ave NE	YES	11	19
793	Old Meadow Rd & Cathy Ln	YES	0	12

STATION_TYPE	classic	lightweight
ZONE_NAME
ANC 1A	10	0
ANC 1B	18	0
ANC 1C	9	2
ANC 1D	2	0
ANC 2A	28	6
ANC 2B	24	0
ANC 2C	25	3
ANC 2E	10	1
ANC 2F	13	0
ANC 3B	3	0
ANC 3C	9	0
ANC 3D	9	0
ANC 3E	6	0
ANC 3F	4	0
ANC 3G	3	0
ANC 4A	5	0
ANC 4B	6	0
ANC 4C	7	1
ANC 4D	5	0
ANC 5A	6	0
ANC 5B	8	0
ANC 5C	8	0
ANC 5D	11	0
ANC 5E	17	0
ANC 6A	11	1
ANC 6B	14	2
ANC 6C	21	0
ANC 6D	28	3
ANC 6E	9	0
ANC 7B	7	0
ANC 7C	5	0
ANC 7D	8	0
ANC 7E	4	0
ANC 7F	4	0
ANC 8A	10	1
ANC 8B	4	0
ANC 8C	3	0
ANC 8D	5	0
ANC 8E	4	0

	NAME	RPP_ZONE	ANC_ID	geometry
0	ANC 4A	4	4A	POLYGON ((-77.03331 38.98999, -77.03332 38.99,...
1	ANC 3G	3	3G	POLYGON ((-77.05161 38.98615, -77.05181 38.986...
2	ANC 4B	4	4B	POLYGON ((-76.99908 38.96328, -76.99909 38.963...
3	ANC 3F	3	3F	POLYGON ((-77.04702 38.96038, -77.04722 38.960...
4	ANC 4C	4	4C	POLYGON ((-77.02803 38.95622, -77.02804 38.956...